Overview

Dataset statistics

Number of variables87
Number of observations2310
Missing cells4260
Missing cells (%)2.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.7 MiB
Average record size in memory760.8 B

Variable types

Categorical60
Numeric27

Alerts

HANDEDNESS has constant value ""Constant
pre_csq4 has constant value ""Constant
pre_csq5 has constant value ""Constant
pre_csq10 has constant value ""Constant
pre_csq13 has constant value ""Constant
pre_csq16 has constant value ""Constant
levelCounter is highly overall correlated with correctCounter and 1 other fieldsHigh correlation
time_ms is highly overall correlated with post_csq5High correlation
correctCounter is highly overall correlated with levelCounter and 2 other fieldsHigh correlation
AGE is highly overall correlated with ptcp and 13 other fieldsHigh correlation
pre is highly overall correlated with ptcp and 31 other fieldsHigh correlation
post is highly overall correlated with ptcp and 50 other fieldsHigh correlation
body° is highly overall correlated with ptcp and 32 other fieldsHigh correlation
post_VRF1 is highly overall correlated with post_VRF9 and 20 other fieldsHigh correlation
post_VRF2 is highly overall correlated with ptcp and 23 other fieldsHigh correlation
post_VRF4 is highly overall correlated with post_VRF15 and 30 other fieldsHigh correlation
post_VRF5 is highly overall correlated with post_VRF18 and 34 other fieldsHigh correlation
post_VRF6 is highly overall correlated with post_VRF7 and 24 other fieldsHigh correlation
post_VRF7 is highly overall correlated with post_VRF6 and 42 other fieldsHigh correlation
post_VRF9 is highly overall correlated with post_VRF1 and 30 other fieldsHigh correlation
post_VRF10 is highly overall correlated with post_VRF6 and 23 other fieldsHigh correlation
post_VRF11 is highly overall correlated with post_VRF7 and 28 other fieldsHigh correlation
post_VRF12 is highly overall correlated with post_VRF9 and 38 other fieldsHigh correlation
post_VRF13 is highly overall correlated with post_VRF6 and 28 other fieldsHigh correlation
post_VRF15 is highly overall correlated with post_VRF4 and 31 other fieldsHigh correlation
post_VRF16 is highly overall correlated with ptcp and 26 other fieldsHigh correlation
post_VRF17 is highly overall correlated with post_VRF9 and 37 other fieldsHigh correlation
post_VRF18 is highly overall correlated with post_VRF5 and 36 other fieldsHigh correlation
post_VRF20 is highly overall correlated with post_VRF7 and 21 other fieldsHigh correlation
post_VRF22 is highly overall correlated with ptcp and 24 other fieldsHigh correlation
post_VRF23 is highly overall correlated with post_VRF5 and 19 other fieldsHigh correlation
post_VRF25 is highly overall correlated with ptcp and 21 other fieldsHigh correlation
post_VRF26 is highly overall correlated with post_VRF7 and 30 other fieldsHigh correlation
ptcp is highly overall correlated with AGE and 73 other fieldsHigh correlation
trial_set is highly overall correlated with post_csq5High correlation
feedbackType is highly overall correlated with post_csq5High correlation
PARTICIPANT ID is highly overall correlated with AGE and 73 other fieldsHigh correlation
SEX is highly overall correlated with pre and 17 other fieldsHigh correlation
pre_csq1 is highly overall correlated with AGE and 28 other fieldsHigh correlation
pre_csq2 is highly overall correlated with pre and 25 other fieldsHigh correlation
pre_csq3 is highly overall correlated with post and 32 other fieldsHigh correlation
pre_csq6 is highly overall correlated with post and 23 other fieldsHigh correlation
pre_csq7 is highly overall correlated with AGE and 16 other fieldsHigh correlation
pre_csq8 is highly overall correlated with pre and 12 other fieldsHigh correlation
pre_csq9 is highly overall correlated with pre and 18 other fieldsHigh correlation
pre_csq11 is highly overall correlated with AGE and 25 other fieldsHigh correlation
pre_csq12 is highly overall correlated with AGE and 25 other fieldsHigh correlation
pre_csq14 is highly overall correlated with AGE and 27 other fieldsHigh correlation
pre_csq15 is highly overall correlated with pre and 25 other fieldsHigh correlation
post_csq1 is highly overall correlated with post and 31 other fieldsHigh correlation
post_csq2 is highly overall correlated with pre and 33 other fieldsHigh correlation
post_csq3 is highly overall correlated with post and 15 other fieldsHigh correlation
post_csq4 is highly overall correlated with post and 23 other fieldsHigh correlation
post_csq5 is highly overall correlated with levelCounter and 79 other fieldsHigh correlation
post_csq6 is highly overall correlated with AGE and 29 other fieldsHigh correlation
post_csq7 is highly overall correlated with AGE and 18 other fieldsHigh correlation
post_csq8 is highly overall correlated with AGE and 25 other fieldsHigh correlation
post_csq9 is highly overall correlated with post and 15 other fieldsHigh correlation
post_csq10 is highly overall correlated with pre and 20 other fieldsHigh correlation
post_csq11 is highly overall correlated with post and 22 other fieldsHigh correlation
post_csq12 is highly overall correlated with AGE and 25 other fieldsHigh correlation
post_csq13 is highly overall correlated with post and 15 other fieldsHigh correlation
post_csq14 is highly overall correlated with post and 33 other fieldsHigh correlation
post_csq15 is highly overall correlated with pre and 35 other fieldsHigh correlation
post_csq16 is highly overall correlated with post and 15 other fieldsHigh correlation
EHQ1 is highly overall correlated with post and 18 other fieldsHigh correlation
EHQ2 is highly overall correlated with pre and 22 other fieldsHigh correlation
EHQ3 is highly overall correlated with pre and 25 other fieldsHigh correlation
EHQ4 is highly overall correlated with pre and 19 other fieldsHigh correlation
EHQ5 is highly overall correlated with pre and 16 other fieldsHigh correlation
EHQ6 is highly overall correlated with pre and 20 other fieldsHigh correlation
EHQ7 is highly overall correlated with pre and 26 other fieldsHigh correlation
EHQ8 is highly overall correlated with pre and 25 other fieldsHigh correlation
EHQ9 is highly overall correlated with pre and 25 other fieldsHigh correlation
EHQ10 is highly overall correlated with post and 31 other fieldsHigh correlation
EHQI is highly overall correlated with pre and 24 other fieldsHigh correlation
EHQII is highly overall correlated with pre and 19 other fieldsHigh correlation
EHQ_F is highly overall correlated with AGE and 67 other fieldsHigh correlation
post_VRF3 is highly overall correlated with pre and 17 other fieldsHigh correlation
post_VRF8 is highly overall correlated with pre and 30 other fieldsHigh correlation
post_VRF14 is highly overall correlated with pre and 38 other fieldsHigh correlation
post_VRF19 is highly overall correlated with pre and 31 other fieldsHigh correlation
post_VRF21 is highly overall correlated with pre and 26 other fieldsHigh correlation
post_VRF24 is highly overall correlated with pre and 32 other fieldsHigh correlation
post_VRF27 is highly overall correlated with pre and 30 other fieldsHigh correlation
EHQ1_F is highly overall correlated with AGE and 67 other fieldsHigh correlation
mistake_flag is highly overall correlated with correctCounter and 1 other fieldsHigh correlation
pre_csq1 is highly imbalanced (55.8%)Imbalance
pre_csq7 is highly imbalanced (73.3%)Imbalance
pre_csq8 is highly imbalanced (73.3%)Imbalance
pre_csq11 is highly imbalanced (73.3%)Imbalance
pre_csq12 is highly imbalanced (73.3%)Imbalance
pre_csq14 is highly imbalanced (56.1%)Imbalance
post_csq5 is highly imbalanced (73.3%)Imbalance
post_csq8 is highly imbalanced (66.5%)Imbalance
post_csq9 is highly imbalanced (66.5%)Imbalance
post_csq10 is highly imbalanced (66.5%)Imbalance
post_csq12 is highly imbalanced (66.5%)Imbalance
post_csq13 is highly imbalanced (66.5%)Imbalance
post_csq14 is highly imbalanced (60.5%)Imbalance
post_csq16 is highly imbalanced (66.5%)Imbalance
EHQ1 is highly imbalanced (73.3%)Imbalance
EHQ2 is highly imbalanced (55.8%)Imbalance
mistake_flag is highly imbalanced (62.9%)Imbalance
correctCounter has 165 (7.1%) missing valuesMissing
post has 210 (9.1%) missing valuesMissing
EHQ_F has 840 (36.4%) missing valuesMissing
post_VRF1 has 105 (4.5%) missing valuesMissing
post_VRF2 has 105 (4.5%) missing valuesMissing
post_VRF4 has 105 (4.5%) missing valuesMissing
post_VRF5 has 105 (4.5%) missing valuesMissing
post_VRF6 has 105 (4.5%) missing valuesMissing
post_VRF7 has 105 (4.5%) missing valuesMissing
post_VRF9 has 105 (4.5%) missing valuesMissing
post_VRF10 has 105 (4.5%) missing valuesMissing
post_VRF11 has 105 (4.5%) missing valuesMissing
post_VRF12 has 105 (4.5%) missing valuesMissing
post_VRF13 has 105 (4.5%) missing valuesMissing
post_VRF15 has 105 (4.5%) missing valuesMissing
post_VRF16 has 105 (4.5%) missing valuesMissing
post_VRF17 has 105 (4.5%) missing valuesMissing
post_VRF18 has 105 (4.5%) missing valuesMissing
post_VRF20 has 105 (4.5%) missing valuesMissing
post_VRF22 has 105 (4.5%) missing valuesMissing
post_VRF23 has 105 (4.5%) missing valuesMissing
post_VRF25 has 105 (4.5%) missing valuesMissing
post_VRF26 has 105 (4.5%) missing valuesMissing
post_VRF27 has 105 (4.5%) missing valuesMissing
EHQ1_F has 840 (36.4%) missing valuesMissing
ptcp is uniformly distributedUniform
trial_set is uniformly distributedUniform
PARTICIPANT ID is uniformly distributedUniform
pre_csq2 is uniformly distributedUniform
levelCounter has 66 (2.9%) zerosZeros
correctCounter has 66 (2.9%) zerosZeros

Reproduction

Analysis started2023-10-20 14:24:42.192601
Analysis finished2023-10-20 14:27:23.375884
Duration2 minutes and 41.18 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

ptcp
Categorical

HIGH CORRELATION  UNIFORM 

Distinct22
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size100.6 KiB
tsvr27
 
105
tsvr26
 
105
tsvr25
 
105
tsvr24
 
105
tsvr23
 
105
Other values (17)
1785 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters13860
Distinct characters14
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowtsvr27
2nd rowtsvr27
3rd rowtsvr27
4th rowtsvr27
5th rowtsvr27

Common Values

ValueCountFrequency (%)
tsvr27 105
 
4.5%
tsvr26 105
 
4.5%
tsvr25 105
 
4.5%
tsvr24 105
 
4.5%
tsvr23 105
 
4.5%
tsvr22 105
 
4.5%
tsvr21 105
 
4.5%
tsvr20 105
 
4.5%
tsvr19 105
 
4.5%
tsvr18 105
 
4.5%
Other values (12) 1260
54.5%

Length

2023-10-20T16:27:23.470528image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
tsvr27 105
 
4.5%
tsvr26 105
 
4.5%
tsvr08 105
 
4.5%
tsvr09 105
 
4.5%
tsvr10 105
 
4.5%
tsvr06 105
 
4.5%
tsvr11 105
 
4.5%
tsvr12 105
 
4.5%
tsvr13 105
 
4.5%
tsvr14 105
 
4.5%
Other values (12) 1260
54.5%

Most occurring characters

ValueCountFrequency (%)
t 2310
16.7%
s 2310
16.7%
v 2310
16.7%
r 2310
16.7%
1 1260
9.1%
2 1050
7.6%
0 630
 
4.5%
7 315
 
2.3%
6 315
 
2.3%
5 210
 
1.5%
Other values (4) 840
 
6.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 9240
66.7%
Decimal Number 4620
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1260
27.3%
2 1050
22.7%
0 630
13.6%
7 315
 
6.8%
6 315
 
6.8%
5 210
 
4.5%
4 210
 
4.5%
3 210
 
4.5%
9 210
 
4.5%
8 210
 
4.5%
Lowercase Letter
ValueCountFrequency (%)
t 2310
25.0%
s 2310
25.0%
v 2310
25.0%
r 2310
25.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 9240
66.7%
Common 4620
33.3%

Most frequent character per script

Common
ValueCountFrequency (%)
1 1260
27.3%
2 1050
22.7%
0 630
13.6%
7 315
 
6.8%
6 315
 
6.8%
5 210
 
4.5%
4 210
 
4.5%
3 210
 
4.5%
9 210
 
4.5%
8 210
 
4.5%
Latin
ValueCountFrequency (%)
t 2310
25.0%
s 2310
25.0%
v 2310
25.0%
r 2310
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 13860
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t 2310
16.7%
s 2310
16.7%
v 2310
16.7%
r 2310
16.7%
1 1260
9.1%
2 1050
7.6%
0 630
 
4.5%
7 315
 
2.3%
6 315
 
2.3%
5 210
 
1.5%
Other values (4) 840
 
6.1%

trial_set
Categorical

HIGH CORRELATION  UNIFORM 

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size85.0 KiB
1
770 
2
770 
3
770 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2310
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 770
33.3%
2 770
33.3%
3 770
33.3%

Length

2023-10-20T16:27:23.616731image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-20T16:27:23.774415image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
1 770
33.3%
2 770
33.3%
3 770
33.3%

Most occurring characters

ValueCountFrequency (%)
1 770
33.3%
2 770
33.3%
3 770
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2310
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 770
33.3%
2 770
33.3%
3 770
33.3%

Most occurring scripts

ValueCountFrequency (%)
Common 2310
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 770
33.3%
2 770
33.3%
3 770
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2310
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 770
33.3%
2 770
33.3%
3 770
33.3%

levelCounter
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct35
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17
Minimum0
Maximum34
Zeros66
Zeros (%)2.9%
Negative0
Negative (%)0.0%
Memory size100.6 KiB
2023-10-20T16:27:23.928003image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q18
median17
Q326
95-th percentile33
Maximum34
Range34
Interquartile range (IQR)18

Descriptive statistics

Standard deviation10.101692
Coefficient of variation (CV)0.59421716
Kurtosis-1.2019646
Mean17
Median Absolute Deviation (MAD)9
Skewness0
Sum39270
Variance102.04417
MonotonicityNot monotonic
2023-10-20T16:27:24.109160image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
0 66
 
2.9%
26 66
 
2.9%
20 66
 
2.9%
21 66
 
2.9%
22 66
 
2.9%
23 66
 
2.9%
24 66
 
2.9%
25 66
 
2.9%
27 66
 
2.9%
18 66
 
2.9%
Other values (25) 1650
71.4%
ValueCountFrequency (%)
0 66
2.9%
1 66
2.9%
2 66
2.9%
3 66
2.9%
4 66
2.9%
5 66
2.9%
6 66
2.9%
7 66
2.9%
8 66
2.9%
9 66
2.9%
ValueCountFrequency (%)
34 66
2.9%
33 66
2.9%
32 66
2.9%
31 66
2.9%
30 66
2.9%
29 66
2.9%
28 66
2.9%
27 66
2.9%
26 66
2.9%
25 66
2.9%

time_ms
Real number (ℝ)

HIGH CORRELATION 

Distinct1023
Distinct (%)44.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5101.4831
Minimum3018
Maximum245815
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size100.6 KiB
2023-10-20T16:27:24.313302image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum3018
5-th percentile3092
Q13166
median3414
Q34226
95-th percentile9929.8
Maximum245815
Range242797
Interquartile range (IQR)1060

Descriptive statistics

Standard deviation8248.155
Coefficient of variation (CV)1.6168151
Kurtosis346.55437
Mean5101.4831
Median Absolute Deviation (MAD)293
Skewness14.775873
Sum11784426
Variance68032060
MonotonicityNot monotonic
2023-10-20T16:27:24.541108image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3129 33
 
1.4%
3166 29
 
1.3%
3158 27
 
1.2%
3141 26
 
1.1%
3174 24
 
1.0%
3121 22
 
1.0%
3084 20
 
0.9%
3117 20
 
0.9%
3154 19
 
0.8%
3137 18
 
0.8%
Other values (1013) 2072
89.7%
ValueCountFrequency (%)
3018 1
 
< 0.1%
3026 1
 
< 0.1%
3034 2
0.1%
3039 2
0.1%
3043 1
 
< 0.1%
3047 1
 
< 0.1%
3048 2
0.1%
3055 2
0.1%
3056 2
0.1%
3059 3
0.1%
ValueCountFrequency (%)
245815 1
< 0.1%
111675 1
< 0.1%
88230 1
< 0.1%
85458 1
< 0.1%
83365 1
< 0.1%
74422 1
< 0.1%
68735 1
< 0.1%
65200 1
< 0.1%
59532 1
< 0.1%
56092 1
< 0.1%

feedbackType
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size100.6 KiB
none
780 
congruent
767 
incongruent
763 

Length

Max length11
Median length9
Mean length7.9722944
Min length4

Characters and Unicode

Total characters18416
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowincongruent
2nd rowcongruent
3rd rowcongruent
4th rownone
5th rowincongruent

Common Values

ValueCountFrequency (%)
none 780
33.8%
congruent 767
33.2%
incongruent 763
33.0%

Length

2023-10-20T16:27:24.746734image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-20T16:27:24.926157image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
none 780
33.8%
congruent 767
33.2%
incongruent 763
33.0%

Most occurring characters

ValueCountFrequency (%)
n 5383
29.2%
o 2310
12.5%
e 2310
12.5%
c 1530
 
8.3%
g 1530
 
8.3%
r 1530
 
8.3%
u 1530
 
8.3%
t 1530
 
8.3%
i 763
 
4.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 18416
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 5383
29.2%
o 2310
12.5%
e 2310
12.5%
c 1530
 
8.3%
g 1530
 
8.3%
r 1530
 
8.3%
u 1530
 
8.3%
t 1530
 
8.3%
i 763
 
4.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 18416
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 5383
29.2%
o 2310
12.5%
e 2310
12.5%
c 1530
 
8.3%
g 1530
 
8.3%
r 1530
 
8.3%
u 1530
 
8.3%
t 1530
 
8.3%
i 763
 
4.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 18416
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 5383
29.2%
o 2310
12.5%
e 2310
12.5%
c 1530
 
8.3%
g 1530
 
8.3%
r 1530
 
8.3%
u 1530
 
8.3%
t 1530
 
8.3%
i 763
 
4.1%

correctCounter
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct35
Distinct (%)1.6%
Missing165
Missing (%)7.1%
Infinite0
Infinite (%)0.0%
Mean15.860606
Minimum0
Maximum34
Zeros66
Zeros (%)2.9%
Negative0
Negative (%)0.0%
Memory size100.6 KiB
2023-10-20T16:27:25.085523image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q18
median16
Q324
95-th percentile31
Maximum34
Range34
Interquartile range (IQR)16

Descriptive statistics

Standard deviation9.5399132
Coefficient of variation (CV)0.60148479
Kurtosis-1.1623762
Mean15.860606
Median Absolute Deviation (MAD)8
Skewness0.041653428
Sum34021
Variance91.009945
MonotonicityNot monotonic
2023-10-20T16:27:25.270490image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
0 66
 
2.9%
10 66
 
2.9%
1 66
 
2.9%
16 66
 
2.9%
15 66
 
2.9%
14 66
 
2.9%
13 66
 
2.9%
12 66
 
2.9%
11 66
 
2.9%
17 66
 
2.9%
Other values (25) 1485
64.3%
(Missing) 165
 
7.1%
ValueCountFrequency (%)
0 66
2.9%
1 66
2.9%
2 66
2.9%
3 66
2.9%
4 66
2.9%
5 66
2.9%
6 66
2.9%
7 66
2.9%
8 66
2.9%
9 66
2.9%
ValueCountFrequency (%)
34 10
 
0.4%
33 27
1.2%
32 44
1.9%
31 52
2.3%
30 56
2.4%
29 59
2.6%
28 62
2.7%
27 63
2.7%
26 64
2.8%
25 65
2.8%

PARTICIPANT ID
Categorical

HIGH CORRELATION  UNIFORM 

Distinct22
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size100.6 KiB
TSVR_27-backup
 
105
TSVR_26-backup
 
105
TSVR_25
 
105
TSVR_24
 
105
TSVR_23
 
105
Other values (17)
1785 

Length

Max length14
Median length7
Mean length7.6363636
Min length7

Characters and Unicode

Total characters17640
Distinct characters22
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTSVR_27-backup
2nd rowTSVR_27-backup
3rd rowTSVR_27-backup
4th rowTSVR_27-backup
5th rowTSVR_27-backup

Common Values

ValueCountFrequency (%)
TSVR_27-backup 105
 
4.5%
TSVR_26-backup 105
 
4.5%
TSVR_25 105
 
4.5%
TSVR_24 105
 
4.5%
TSVR_23 105
 
4.5%
TSVR_22 105
 
4.5%
TSVR_21 105
 
4.5%
TSVR_20 105
 
4.5%
TSVR_19 105
 
4.5%
TSVR_18 105
 
4.5%
Other values (12) 1260
54.5%

Length

2023-10-20T16:27:25.460257image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
tsvr_27-backup 105
 
4.5%
tsvr_26-backup 105
 
4.5%
tsvr_08 105
 
4.5%
tsvr_09 105
 
4.5%
tsvr_10 105
 
4.5%
tsvr_06 105
 
4.5%
tsvr_11 105
 
4.5%
tsvr_12 105
 
4.5%
tsvr_13 105
 
4.5%
tsvr_14 105
 
4.5%
Other values (12) 1260
54.5%

Most occurring characters

ValueCountFrequency (%)
T 2310
13.1%
V 2310
13.1%
R 2310
13.1%
_ 2310
13.1%
S 2310
13.1%
1 1260
7.1%
2 1050
6.0%
0 630
 
3.6%
6 315
 
1.8%
7 315
 
1.8%
Other values (12) 2520
14.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 9240
52.4%
Decimal Number 4620
26.2%
Connector Punctuation 2310
 
13.1%
Lowercase Letter 1260
 
7.1%
Dash Punctuation 210
 
1.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1260
27.3%
2 1050
22.7%
0 630
13.6%
6 315
 
6.8%
7 315
 
6.8%
9 210
 
4.5%
3 210
 
4.5%
4 210
 
4.5%
5 210
 
4.5%
8 210
 
4.5%
Lowercase Letter
ValueCountFrequency (%)
k 210
16.7%
p 210
16.7%
u 210
16.7%
c 210
16.7%
a 210
16.7%
b 210
16.7%
Uppercase Letter
ValueCountFrequency (%)
T 2310
25.0%
V 2310
25.0%
R 2310
25.0%
S 2310
25.0%
Connector Punctuation
ValueCountFrequency (%)
_ 2310
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 210
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 10500
59.5%
Common 7140
40.5%

Most frequent character per script

Common
ValueCountFrequency (%)
_ 2310
32.4%
1 1260
17.6%
2 1050
14.7%
0 630
 
8.8%
6 315
 
4.4%
7 315
 
4.4%
9 210
 
2.9%
3 210
 
2.9%
4 210
 
2.9%
5 210
 
2.9%
Other values (2) 420
 
5.9%
Latin
ValueCountFrequency (%)
T 2310
22.0%
V 2310
22.0%
R 2310
22.0%
S 2310
22.0%
k 210
 
2.0%
p 210
 
2.0%
u 210
 
2.0%
c 210
 
2.0%
a 210
 
2.0%
b 210
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 17640
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
T 2310
13.1%
V 2310
13.1%
R 2310
13.1%
_ 2310
13.1%
S 2310
13.1%
1 1260
7.1%
2 1050
6.0%
0 630
 
3.6%
6 315
 
1.8%
7 315
 
1.8%
Other values (12) 2520
14.3%

HANDEDNESS
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size100.6 KiB
R
2310 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2310
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowR
2nd rowR
3rd rowR
4th rowR
5th rowR

Common Values

ValueCountFrequency (%)
R 2310
100.0%

Length

2023-10-20T16:27:25.628627image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-20T16:27:25.773536image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
r 2310
100.0%

Most occurring characters

ValueCountFrequency (%)
R 2310
100.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 2310
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
R 2310
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 2310
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
R 2310
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2310
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
R 2310
100.0%

SEX
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size100.6 KiB
F
1785 
M
525 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2310
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowF
2nd rowF
3rd rowF
4th rowF
5th rowF

Common Values

ValueCountFrequency (%)
F 1785
77.3%
M 525
 
22.7%

Length

2023-10-20T16:27:25.891998image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-20T16:27:26.042618image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
f 1785
77.3%
m 525
 
22.7%

Most occurring characters

ValueCountFrequency (%)
F 1785
77.3%
M 525
 
22.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 2310
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
F 1785
77.3%
M 525
 
22.7%

Most occurring scripts

ValueCountFrequency (%)
Latin 2310
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
F 1785
77.3%
M 525
 
22.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2310
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
F 1785
77.3%
M 525
 
22.7%

AGE
Real number (ℝ)

HIGH CORRELATION 

Distinct12
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.136364
Minimum18
Maximum50
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size100.6 KiB
2023-10-20T16:27:26.161241image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum18
5-th percentile19
Q122
median24
Q327
95-th percentile30
Maximum50
Range32
Interquartile range (IQR)5

Descriptive statistics

Standard deviation6.2775437
Coefficient of variation (CV)0.24973953
Kurtosis8.4129491
Mean25.136364
Median Absolute Deviation (MAD)3
Skewness2.6707431
Sum58065
Variance39.407555
MonotonicityNot monotonic
2023-10-20T16:27:26.308128image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
23 420
18.2%
27 315
13.6%
28 210
9.1%
20 210
9.1%
22 210
9.1%
26 210
9.1%
25 210
9.1%
50 105
 
4.5%
21 105
 
4.5%
18 105
 
4.5%
Other values (2) 210
9.1%
ValueCountFrequency (%)
18 105
 
4.5%
19 105
 
4.5%
20 210
9.1%
21 105
 
4.5%
22 210
9.1%
23 420
18.2%
25 210
9.1%
26 210
9.1%
27 315
13.6%
28 210
9.1%
ValueCountFrequency (%)
50 105
 
4.5%
30 105
 
4.5%
28 210
9.1%
27 315
13.6%
26 210
9.1%
25 210
9.1%
23 420
18.2%
22 210
9.1%
21 105
 
4.5%
20 210
9.1%

pre
Real number (ℝ)

HIGH CORRELATION 

Distinct17
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean57.272727
Minimum22
Maximum113
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size100.6 KiB
2023-10-20T16:27:26.473055image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum22
5-th percentile36
Q144
median56
Q365
95-th percentile82
Maximum113
Range91
Interquartile range (IQR)21

Descriptive statistics

Standard deviation19.006942
Coefficient of variation (CV)0.33186723
Kurtosis1.3887538
Mean57.272727
Median Absolute Deviation (MAD)11.5
Skewness0.9685041
Sum132300
Variance361.26383
MonotonicityNot monotonic
2023-10-20T16:27:26.627977image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
56 420
18.2%
44 210
 
9.1%
57 210
 
9.1%
113 105
 
4.5%
65 105
 
4.5%
41 105
 
4.5%
43 105
 
4.5%
46 105
 
4.5%
36 105
 
4.5%
48 105
 
4.5%
Other values (7) 735
31.8%
ValueCountFrequency (%)
22 105
 
4.5%
36 105
 
4.5%
41 105
 
4.5%
43 105
 
4.5%
44 210
9.1%
45 105
 
4.5%
46 105
 
4.5%
48 105
 
4.5%
56 420
18.2%
57 210
9.1%
ValueCountFrequency (%)
113 105
 
4.5%
82 105
 
4.5%
81 105
 
4.5%
78 105
 
4.5%
74 105
 
4.5%
65 105
 
4.5%
60 105
 
4.5%
57 210
9.1%
56 420
18.2%
48 105
 
4.5%

post
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct17
Distinct (%)0.8%
Missing210
Missing (%)9.1%
Infinite0
Infinite (%)0.0%
Mean49.25
Minimum12
Maximum92
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size100.6 KiB
2023-10-20T16:27:26.781021image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum12
5-th percentile17.7
Q137.5
median51
Q356.75
95-th percentile82.5
Maximum92
Range80
Interquartile range (IQR)19.25

Descriptive statistics

Standard deviation19.546597
Coefficient of variation (CV)0.39688521
Kurtosis-0.1198705
Mean49.25
Median Absolute Deviation (MAD)9
Skewness0.04823589
Sum103425
Variance382.06944
MonotonicityNot monotonic
2023-10-20T16:27:26.936147image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
56 315
13.6%
51 210
 
9.1%
18 105
 
4.5%
82 105
 
4.5%
28 105
 
4.5%
49 105
 
4.5%
33 105
 
4.5%
39 105
 
4.5%
92 105
 
4.5%
12 105
 
4.5%
Other values (7) 735
31.8%
(Missing) 210
 
9.1%
ValueCountFrequency (%)
12 105
4.5%
18 105
4.5%
22 105
4.5%
28 105
4.5%
33 105
4.5%
39 105
4.5%
46 105
4.5%
49 105
4.5%
50 105
4.5%
51 210
9.1%
ValueCountFrequency (%)
92 105
 
4.5%
82 105
 
4.5%
69 105
 
4.5%
61 105
 
4.5%
59 105
 
4.5%
56 315
13.6%
55 105
 
4.5%
51 210
9.1%
50 105
 
4.5%
49 105
 
4.5%

body°
Real number (ℝ)

HIGH CORRELATION 

Distinct9
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.209091
Minimum35.8
Maximum37.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size100.6 KiB
2023-10-20T16:27:27.095656image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum35.8
5-th percentile35.9
Q136
median36.2
Q336.2
95-th percentile36.8
Maximum37.1
Range1.3
Interquartile range (IQR)0.2

Descriptive statistics

Standard deviation0.29687932
Coefficient of variation (CV)0.0081990271
Kurtosis2.0369263
Mean36.209091
Median Absolute Deviation (MAD)0.15
Skewness1.5291562
Sum83643
Variance0.088137328
MonotonicityNot monotonic
2023-10-20T16:27:27.246042image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
36.2 840
36.4%
36 630
27.3%
36.3 210
 
9.1%
36.8 105
 
4.5%
36.1 105
 
4.5%
35.9 105
 
4.5%
35.8 105
 
4.5%
37.1 105
 
4.5%
36.7 105
 
4.5%
ValueCountFrequency (%)
35.8 105
 
4.5%
35.9 105
 
4.5%
36 630
27.3%
36.1 105
 
4.5%
36.2 840
36.4%
36.3 210
 
9.1%
36.7 105
 
4.5%
36.8 105
 
4.5%
37.1 105
 
4.5%
ValueCountFrequency (%)
37.1 105
 
4.5%
36.8 105
 
4.5%
36.7 105
 
4.5%
36.3 210
 
9.1%
36.2 840
36.4%
36.1 105
 
4.5%
36 630
27.3%
35.9 105
 
4.5%
35.8 105
 
4.5%

pre_csq1
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size100.6 KiB
1
1995 
2
210 
<NA>
 
105

Length

Max length4
Median length1
Mean length1.1363636
Min length1

Characters and Unicode

Total characters2625
Distinct characters6
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 1995
86.4%
2 210
 
9.1%
<NA> 105
 
4.5%

Length

2023-10-20T16:27:27.414294image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-20T16:27:27.582798image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
1 1995
86.4%
2 210
 
9.1%
na 105
 
4.5%

Most occurring characters

ValueCountFrequency (%)
1 1995
76.0%
2 210
 
8.0%
< 105
 
4.0%
N 105
 
4.0%
A 105
 
4.0%
> 105
 
4.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2205
84.0%
Math Symbol 210
 
8.0%
Uppercase Letter 210
 
8.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1995
90.5%
2 210
 
9.5%
Math Symbol
ValueCountFrequency (%)
< 105
50.0%
> 105
50.0%
Uppercase Letter
ValueCountFrequency (%)
N 105
50.0%
A 105
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2415
92.0%
Latin 210
 
8.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 1995
82.6%
2 210
 
8.7%
< 105
 
4.3%
> 105
 
4.3%
Latin
ValueCountFrequency (%)
N 105
50.0%
A 105
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2625
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 1995
76.0%
2 210
 
8.0%
< 105
 
4.0%
N 105
 
4.0%
A 105
 
4.0%
> 105
 
4.0%

pre_csq2
Categorical

HIGH CORRELATION  UNIFORM 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size100.6 KiB
1
1155 
2
1155 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2310
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 1155
50.0%
2 1155
50.0%

Length

2023-10-20T16:27:27.715959image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-20T16:27:27.863477image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
1 1155
50.0%
2 1155
50.0%

Most occurring characters

ValueCountFrequency (%)
1 1155
50.0%
2 1155
50.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2310
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1155
50.0%
2 1155
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2310
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 1155
50.0%
2 1155
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2310
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 1155
50.0%
2 1155
50.0%

pre_csq3
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size100.6 KiB
1
1890 
2
315 
<NA>
 
105

Length

Max length4
Median length1
Mean length1.1363636
Min length1

Characters and Unicode

Total characters2625
Distinct characters6
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 1890
81.8%
2 315
 
13.6%
<NA> 105
 
4.5%

Length

2023-10-20T16:27:27.995330image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-20T16:27:28.157615image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
1 1890
81.8%
2 315
 
13.6%
na 105
 
4.5%

Most occurring characters

ValueCountFrequency (%)
1 1890
72.0%
2 315
 
12.0%
< 105
 
4.0%
N 105
 
4.0%
A 105
 
4.0%
> 105
 
4.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2205
84.0%
Math Symbol 210
 
8.0%
Uppercase Letter 210
 
8.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1890
85.7%
2 315
 
14.3%
Math Symbol
ValueCountFrequency (%)
< 105
50.0%
> 105
50.0%
Uppercase Letter
ValueCountFrequency (%)
N 105
50.0%
A 105
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2415
92.0%
Latin 210
 
8.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 1890
78.3%
2 315
 
13.0%
< 105
 
4.3%
> 105
 
4.3%
Latin
ValueCountFrequency (%)
N 105
50.0%
A 105
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2625
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 1890
72.0%
2 315
 
12.0%
< 105
 
4.0%
N 105
 
4.0%
A 105
 
4.0%
> 105
 
4.0%

pre_csq4
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size100.6 KiB
1
2310 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2310
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 2310
100.0%

Length

2023-10-20T16:27:28.294944image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-20T16:27:28.445810image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
1 2310
100.0%

Most occurring characters

ValueCountFrequency (%)
1 2310
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2310
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 2310
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2310
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 2310
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2310
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 2310
100.0%

pre_csq5
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size100.6 KiB
1
2310 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2310
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 2310
100.0%

Length

2023-10-20T16:27:28.567305image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-20T16:27:28.718588image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
1 2310
100.0%

Most occurring characters

ValueCountFrequency (%)
1 2310
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2310
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 2310
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2310
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 2310
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2310
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 2310
100.0%

pre_csq6
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size100.6 KiB
1
1890 
2
420 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2310
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 1890
81.8%
2 420
 
18.2%

Length

2023-10-20T16:27:28.834097image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-20T16:27:28.983753image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
1 1890
81.8%
2 420
 
18.2%

Most occurring characters

ValueCountFrequency (%)
1 1890
81.8%
2 420
 
18.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2310
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1890
81.8%
2 420
 
18.2%

Most occurring scripts

ValueCountFrequency (%)
Common 2310
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 1890
81.8%
2 420
 
18.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2310
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 1890
81.8%
2 420
 
18.2%

pre_csq7
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size100.6 KiB
1
2205 
2
 
105

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2310
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 2205
95.5%
2 105
 
4.5%

Length

2023-10-20T16:27:29.108400image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-20T16:27:29.255375image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
1 2205
95.5%
2 105
 
4.5%

Most occurring characters

ValueCountFrequency (%)
1 2205
95.5%
2 105
 
4.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2310
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 2205
95.5%
2 105
 
4.5%

Most occurring scripts

ValueCountFrequency (%)
Common 2310
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 2205
95.5%
2 105
 
4.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2310
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 2205
95.5%
2 105
 
4.5%

pre_csq8
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size100.6 KiB
1
2205 
2
 
105

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2310
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 2205
95.5%
2 105
 
4.5%

Length

2023-10-20T16:27:29.385725image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-20T16:27:29.538178image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
1 2205
95.5%
2 105
 
4.5%

Most occurring characters

ValueCountFrequency (%)
1 2205
95.5%
2 105
 
4.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2310
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 2205
95.5%
2 105
 
4.5%

Most occurring scripts

ValueCountFrequency (%)
Common 2310
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 2205
95.5%
2 105
 
4.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2310
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 2205
95.5%
2 105
 
4.5%

pre_csq9
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size100.6 KiB
1
1995 
2
315 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2310
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 1995
86.4%
2 315
 
13.6%

Length

2023-10-20T16:27:29.662946image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-20T16:27:29.811873image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
1 1995
86.4%
2 315
 
13.6%

Most occurring characters

ValueCountFrequency (%)
1 1995
86.4%
2 315
 
13.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2310
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1995
86.4%
2 315
 
13.6%

Most occurring scripts

ValueCountFrequency (%)
Common 2310
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 1995
86.4%
2 315
 
13.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2310
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 1995
86.4%
2 315
 
13.6%

pre_csq10
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size100.6 KiB
1
2310 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2310
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 2310
100.0%

Length

2023-10-20T16:27:29.939846image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-20T16:27:30.083993image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
1 2310
100.0%

Most occurring characters

ValueCountFrequency (%)
1 2310
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2310
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 2310
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2310
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 2310
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2310
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 2310
100.0%

pre_csq11
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size100.6 KiB
1
2205 
2
 
105

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2310
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 2205
95.5%
2 105
 
4.5%

Length

2023-10-20T16:27:30.202236image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-20T16:27:30.350520image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
1 2205
95.5%
2 105
 
4.5%

Most occurring characters

ValueCountFrequency (%)
1 2205
95.5%
2 105
 
4.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2310
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 2205
95.5%
2 105
 
4.5%

Most occurring scripts

ValueCountFrequency (%)
Common 2310
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 2205
95.5%
2 105
 
4.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2310
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 2205
95.5%
2 105
 
4.5%

pre_csq12
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size100.6 KiB
1
2205 
2
 
105

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2310
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 2205
95.5%
2 105
 
4.5%

Length

2023-10-20T16:27:30.479675image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-20T16:27:30.633381image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
1 2205
95.5%
2 105
 
4.5%

Most occurring characters

ValueCountFrequency (%)
1 2205
95.5%
2 105
 
4.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2310
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 2205
95.5%
2 105
 
4.5%

Most occurring scripts

ValueCountFrequency (%)
Common 2310
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 2205
95.5%
2 105
 
4.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2310
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 2205
95.5%
2 105
 
4.5%

pre_csq13
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size100.6 KiB
1
2310 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2310
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 2310
100.0%

Length

2023-10-20T16:27:30.757680image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-20T16:27:30.901844image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
1 2310
100.0%

Most occurring characters

ValueCountFrequency (%)
1 2310
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2310
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 2310
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2310
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 2310
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2310
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 2310
100.0%

pre_csq14
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size100.6 KiB
1
2100 
2
 
210

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2310
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 2100
90.9%
2 210
 
9.1%

Length

2023-10-20T16:27:31.018610image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-20T16:27:31.174505image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
1 2100
90.9%
2 210
 
9.1%

Most occurring characters

ValueCountFrequency (%)
1 2100
90.9%
2 210
 
9.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2310
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 2100
90.9%
2 210
 
9.1%

Most occurring scripts

ValueCountFrequency (%)
Common 2310
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 2100
90.9%
2 210
 
9.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2310
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 2100
90.9%
2 210
 
9.1%

pre_csq15
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size100.6 KiB
1
1575 
2
525 
3
210 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2310
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 1575
68.2%
2 525
 
22.7%
3 210
 
9.1%

Length

2023-10-20T16:27:31.303087image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-20T16:27:31.458034image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
1 1575
68.2%
2 525
 
22.7%
3 210
 
9.1%

Most occurring characters

ValueCountFrequency (%)
1 1575
68.2%
2 525
 
22.7%
3 210
 
9.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2310
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1575
68.2%
2 525
 
22.7%
3 210
 
9.1%

Most occurring scripts

ValueCountFrequency (%)
Common 2310
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 1575
68.2%
2 525
 
22.7%
3 210
 
9.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2310
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 1575
68.2%
2 525
 
22.7%
3 210
 
9.1%

pre_csq16
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size100.6 KiB
1
2310 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2310
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 2310
100.0%

Length

2023-10-20T16:27:32.650619image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-20T16:27:32.749520image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
1 2310
100.0%

Most occurring characters

ValueCountFrequency (%)
1 2310
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2310
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 2310
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2310
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 2310
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2310
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 2310
100.0%

post_csq1
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size100.6 KiB
1
1785 
2
315 
<NA>
210 

Length

Max length4
Median length1
Mean length1.2727273
Min length1

Characters and Unicode

Total characters2940
Distinct characters6
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 1785
77.3%
2 315
 
13.6%
<NA> 210
 
9.1%

Length

2023-10-20T16:27:32.836608image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-20T16:27:32.957713image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
1 1785
77.3%
2 315
 
13.6%
na 210
 
9.1%

Most occurring characters

ValueCountFrequency (%)
1 1785
60.7%
2 315
 
10.7%
< 210
 
7.1%
N 210
 
7.1%
A 210
 
7.1%
> 210
 
7.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2100
71.4%
Math Symbol 420
 
14.3%
Uppercase Letter 420
 
14.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1785
85.0%
2 315
 
15.0%
Math Symbol
ValueCountFrequency (%)
< 210
50.0%
> 210
50.0%
Uppercase Letter
ValueCountFrequency (%)
N 210
50.0%
A 210
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2520
85.7%
Latin 420
 
14.3%

Most frequent character per script

Common
ValueCountFrequency (%)
1 1785
70.8%
2 315
 
12.5%
< 210
 
8.3%
> 210
 
8.3%
Latin
ValueCountFrequency (%)
N 210
50.0%
A 210
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2940
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 1785
60.7%
2 315
 
10.7%
< 210
 
7.1%
N 210
 
7.1%
A 210
 
7.1%
> 210
 
7.1%

post_csq2
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size100.6 KiB
1
1050 
2
945 
3
210 
<NA>
 
105

Length

Max length4
Median length1
Mean length1.1363636
Min length1

Characters and Unicode

Total characters2625
Distinct characters7
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
1 1050
45.5%
2 945
40.9%
3 210
 
9.1%
<NA> 105
 
4.5%

Length

2023-10-20T16:27:33.081129image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-20T16:27:33.245145image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
1 1050
45.5%
2 945
40.9%
3 210
 
9.1%
na 105
 
4.5%

Most occurring characters

ValueCountFrequency (%)
1 1050
40.0%
2 945
36.0%
3 210
 
8.0%
< 105
 
4.0%
N 105
 
4.0%
A 105
 
4.0%
> 105
 
4.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2205
84.0%
Math Symbol 210
 
8.0%
Uppercase Letter 210
 
8.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1050
47.6%
2 945
42.9%
3 210
 
9.5%
Math Symbol
ValueCountFrequency (%)
< 105
50.0%
> 105
50.0%
Uppercase Letter
ValueCountFrequency (%)
N 105
50.0%
A 105
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2415
92.0%
Latin 210
 
8.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 1050
43.5%
2 945
39.1%
3 210
 
8.7%
< 105
 
4.3%
> 105
 
4.3%
Latin
ValueCountFrequency (%)
N 105
50.0%
A 105
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2625
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 1050
40.0%
2 945
36.0%
3 210
 
8.0%
< 105
 
4.0%
N 105
 
4.0%
A 105
 
4.0%
> 105
 
4.0%

post_csq3
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size100.6 KiB
1
1680 
2
420 
<NA>
 
105
3
 
105

Length

Max length4
Median length1
Mean length1.1363636
Min length1

Characters and Unicode

Total characters2625
Distinct characters7
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 1680
72.7%
2 420
 
18.2%
<NA> 105
 
4.5%
3 105
 
4.5%

Length

2023-10-20T16:27:33.388217image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-20T16:27:33.555895image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
1 1680
72.7%
2 420
 
18.2%
na 105
 
4.5%
3 105
 
4.5%

Most occurring characters

ValueCountFrequency (%)
1 1680
64.0%
2 420
 
16.0%
< 105
 
4.0%
N 105
 
4.0%
A 105
 
4.0%
> 105
 
4.0%
3 105
 
4.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2205
84.0%
Math Symbol 210
 
8.0%
Uppercase Letter 210
 
8.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1680
76.2%
2 420
 
19.0%
3 105
 
4.8%
Math Symbol
ValueCountFrequency (%)
< 105
50.0%
> 105
50.0%
Uppercase Letter
ValueCountFrequency (%)
N 105
50.0%
A 105
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2415
92.0%
Latin 210
 
8.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 1680
69.6%
2 420
 
17.4%
< 105
 
4.3%
> 105
 
4.3%
3 105
 
4.3%
Latin
ValueCountFrequency (%)
N 105
50.0%
A 105
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2625
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 1680
64.0%
2 420
 
16.0%
< 105
 
4.0%
N 105
 
4.0%
A 105
 
4.0%
> 105
 
4.0%
3 105
 
4.0%

post_csq4
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size100.6 KiB
1
1890 
2
315 
<NA>
 
105

Length

Max length4
Median length1
Mean length1.1363636
Min length1

Characters and Unicode

Total characters2625
Distinct characters6
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 1890
81.8%
2 315
 
13.6%
<NA> 105
 
4.5%

Length

2023-10-20T16:27:33.706571image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-20T16:27:33.875435image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
1 1890
81.8%
2 315
 
13.6%
na 105
 
4.5%

Most occurring characters

ValueCountFrequency (%)
1 1890
72.0%
2 315
 
12.0%
< 105
 
4.0%
N 105
 
4.0%
A 105
 
4.0%
> 105
 
4.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2205
84.0%
Math Symbol 210
 
8.0%
Uppercase Letter 210
 
8.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1890
85.7%
2 315
 
14.3%
Math Symbol
ValueCountFrequency (%)
< 105
50.0%
> 105
50.0%
Uppercase Letter
ValueCountFrequency (%)
N 105
50.0%
A 105
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2415
92.0%
Latin 210
 
8.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 1890
78.3%
2 315
 
13.0%
< 105
 
4.3%
> 105
 
4.3%
Latin
ValueCountFrequency (%)
N 105
50.0%
A 105
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2625
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 1890
72.0%
2 315
 
12.0%
< 105
 
4.0%
N 105
 
4.0%
A 105
 
4.0%
> 105
 
4.0%

post_csq5
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size100.6 KiB
1
2205 
<NA>
 
105

Length

Max length4
Median length1
Mean length1.1363636
Min length1

Characters and Unicode

Total characters2625
Distinct characters5
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 2205
95.5%
<NA> 105
 
4.5%

Length

2023-10-20T16:27:34.017647image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-20T16:27:34.177165image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
1 2205
95.5%
na 105
 
4.5%

Most occurring characters

ValueCountFrequency (%)
1 2205
84.0%
< 105
 
4.0%
N 105
 
4.0%
A 105
 
4.0%
> 105
 
4.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2205
84.0%
Math Symbol 210
 
8.0%
Uppercase Letter 210
 
8.0%

Most frequent character per category

Math Symbol
ValueCountFrequency (%)
< 105
50.0%
> 105
50.0%
Uppercase Letter
ValueCountFrequency (%)
N 105
50.0%
A 105
50.0%
Decimal Number
ValueCountFrequency (%)
1 2205
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2415
92.0%
Latin 210
 
8.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 2205
91.3%
< 105
 
4.3%
> 105
 
4.3%
Latin
ValueCountFrequency (%)
N 105
50.0%
A 105
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2625
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 2205
84.0%
< 105
 
4.0%
N 105
 
4.0%
A 105
 
4.0%
> 105
 
4.0%

post_csq6
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size100.6 KiB
1
1680 
2
420 
<NA>
 
105
3
 
105

Length

Max length4
Median length1
Mean length1.1363636
Min length1

Characters and Unicode

Total characters2625
Distinct characters7
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 1680
72.7%
2 420
 
18.2%
<NA> 105
 
4.5%
3 105
 
4.5%

Length

2023-10-20T16:27:34.308026image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-20T16:27:34.475845image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
1 1680
72.7%
2 420
 
18.2%
na 105
 
4.5%
3 105
 
4.5%

Most occurring characters

ValueCountFrequency (%)
1 1680
64.0%
2 420
 
16.0%
< 105
 
4.0%
N 105
 
4.0%
A 105
 
4.0%
> 105
 
4.0%
3 105
 
4.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2205
84.0%
Math Symbol 210
 
8.0%
Uppercase Letter 210
 
8.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1680
76.2%
2 420
 
19.0%
3 105
 
4.8%
Math Symbol
ValueCountFrequency (%)
< 105
50.0%
> 105
50.0%
Uppercase Letter
ValueCountFrequency (%)
N 105
50.0%
A 105
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2415
92.0%
Latin 210
 
8.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 1680
69.6%
2 420
 
17.4%
< 105
 
4.3%
> 105
 
4.3%
3 105
 
4.3%
Latin
ValueCountFrequency (%)
N 105
50.0%
A 105
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2625
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 1680
64.0%
2 420
 
16.0%
< 105
 
4.0%
N 105
 
4.0%
A 105
 
4.0%
> 105
 
4.0%
3 105
 
4.0%

post_csq7
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size100.6 KiB
1
1785 
2
420 
<NA>
 
105

Length

Max length4
Median length1
Mean length1.1363636
Min length1

Characters and Unicode

Total characters2625
Distinct characters6
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 1785
77.3%
2 420
 
18.2%
<NA> 105
 
4.5%

Length

2023-10-20T16:27:34.624345image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-20T16:27:34.792330image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
1 1785
77.3%
2 420
 
18.2%
na 105
 
4.5%

Most occurring characters

ValueCountFrequency (%)
1 1785
68.0%
2 420
 
16.0%
< 105
 
4.0%
N 105
 
4.0%
A 105
 
4.0%
> 105
 
4.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2205
84.0%
Math Symbol 210
 
8.0%
Uppercase Letter 210
 
8.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1785
81.0%
2 420
 
19.0%
Math Symbol
ValueCountFrequency (%)
< 105
50.0%
> 105
50.0%
Uppercase Letter
ValueCountFrequency (%)
N 105
50.0%
A 105
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2415
92.0%
Latin 210
 
8.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 1785
73.9%
2 420
 
17.4%
< 105
 
4.3%
> 105
 
4.3%
Latin
ValueCountFrequency (%)
N 105
50.0%
A 105
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2625
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 1785
68.0%
2 420
 
16.0%
< 105
 
4.0%
N 105
 
4.0%
A 105
 
4.0%
> 105
 
4.0%

post_csq8
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size100.6 KiB
1
2100 
<NA>
 
105
2
 
105

Length

Max length4
Median length1
Mean length1.1363636
Min length1

Characters and Unicode

Total characters2625
Distinct characters6
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 2100
90.9%
<NA> 105
 
4.5%
2 105
 
4.5%

Length

2023-10-20T16:27:34.943116image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-20T16:27:35.115074image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
1 2100
90.9%
na 105
 
4.5%
2 105
 
4.5%

Most occurring characters

ValueCountFrequency (%)
1 2100
80.0%
< 105
 
4.0%
N 105
 
4.0%
A 105
 
4.0%
> 105
 
4.0%
2 105
 
4.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2205
84.0%
Math Symbol 210
 
8.0%
Uppercase Letter 210
 
8.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 2100
95.2%
2 105
 
4.8%
Math Symbol
ValueCountFrequency (%)
< 105
50.0%
> 105
50.0%
Uppercase Letter
ValueCountFrequency (%)
N 105
50.0%
A 105
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2415
92.0%
Latin 210
 
8.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 2100
87.0%
< 105
 
4.3%
> 105
 
4.3%
2 105
 
4.3%
Latin
ValueCountFrequency (%)
N 105
50.0%
A 105
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2625
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 2100
80.0%
< 105
 
4.0%
N 105
 
4.0%
A 105
 
4.0%
> 105
 
4.0%
2 105
 
4.0%

post_csq9
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size100.6 KiB
1
2100 
<NA>
 
105
2
 
105

Length

Max length4
Median length1
Mean length1.1363636
Min length1

Characters and Unicode

Total characters2625
Distinct characters6
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 2100
90.9%
<NA> 105
 
4.5%
2 105
 
4.5%

Length

2023-10-20T16:27:35.256881image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-20T16:27:35.421966image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
1 2100
90.9%
na 105
 
4.5%
2 105
 
4.5%

Most occurring characters

ValueCountFrequency (%)
1 2100
80.0%
< 105
 
4.0%
N 105
 
4.0%
A 105
 
4.0%
> 105
 
4.0%
2 105
 
4.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2205
84.0%
Math Symbol 210
 
8.0%
Uppercase Letter 210
 
8.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 2100
95.2%
2 105
 
4.8%
Math Symbol
ValueCountFrequency (%)
< 105
50.0%
> 105
50.0%
Uppercase Letter
ValueCountFrequency (%)
N 105
50.0%
A 105
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2415
92.0%
Latin 210
 
8.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 2100
87.0%
< 105
 
4.3%
> 105
 
4.3%
2 105
 
4.3%
Latin
ValueCountFrequency (%)
N 105
50.0%
A 105
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2625
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 2100
80.0%
< 105
 
4.0%
N 105
 
4.0%
A 105
 
4.0%
> 105
 
4.0%
2 105
 
4.0%

post_csq10
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size100.6 KiB
1
2100 
2
 
105
<NA>
 
105

Length

Max length4
Median length1
Mean length1.1363636
Min length1

Characters and Unicode

Total characters2625
Distinct characters6
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 2100
90.9%
2 105
 
4.5%
<NA> 105
 
4.5%

Length

2023-10-20T16:27:35.566469image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-20T16:27:35.730487image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
1 2100
90.9%
2 105
 
4.5%
na 105
 
4.5%

Most occurring characters

ValueCountFrequency (%)
1 2100
80.0%
2 105
 
4.0%
< 105
 
4.0%
N 105
 
4.0%
A 105
 
4.0%
> 105
 
4.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2205
84.0%
Math Symbol 210
 
8.0%
Uppercase Letter 210
 
8.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 2100
95.2%
2 105
 
4.8%
Math Symbol
ValueCountFrequency (%)
< 105
50.0%
> 105
50.0%
Uppercase Letter
ValueCountFrequency (%)
N 105
50.0%
A 105
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2415
92.0%
Latin 210
 
8.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 2100
87.0%
2 105
 
4.3%
< 105
 
4.3%
> 105
 
4.3%
Latin
ValueCountFrequency (%)
N 105
50.0%
A 105
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2625
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 2100
80.0%
2 105
 
4.0%
< 105
 
4.0%
N 105
 
4.0%
A 105
 
4.0%
> 105
 
4.0%

post_csq11
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size100.6 KiB
1
1680 
2
420 
<NA>
 
105
3
 
105

Length

Max length4
Median length1
Mean length1.1363636
Min length1

Characters and Unicode

Total characters2625
Distinct characters7
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 1680
72.7%
2 420
 
18.2%
<NA> 105
 
4.5%
3 105
 
4.5%

Length

2023-10-20T16:27:35.866026image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-20T16:27:36.036586image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
1 1680
72.7%
2 420
 
18.2%
na 105
 
4.5%
3 105
 
4.5%

Most occurring characters

ValueCountFrequency (%)
1 1680
64.0%
2 420
 
16.0%
< 105
 
4.0%
N 105
 
4.0%
A 105
 
4.0%
> 105
 
4.0%
3 105
 
4.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2205
84.0%
Math Symbol 210
 
8.0%
Uppercase Letter 210
 
8.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1680
76.2%
2 420
 
19.0%
3 105
 
4.8%
Math Symbol
ValueCountFrequency (%)
< 105
50.0%
> 105
50.0%
Uppercase Letter
ValueCountFrequency (%)
N 105
50.0%
A 105
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2415
92.0%
Latin 210
 
8.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 1680
69.6%
2 420
 
17.4%
< 105
 
4.3%
> 105
 
4.3%
3 105
 
4.3%
Latin
ValueCountFrequency (%)
N 105
50.0%
A 105
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2625
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 1680
64.0%
2 420
 
16.0%
< 105
 
4.0%
N 105
 
4.0%
A 105
 
4.0%
> 105
 
4.0%
3 105
 
4.0%

post_csq12
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size100.6 KiB
1
2100 
<NA>
 
105
2
 
105

Length

Max length4
Median length1
Mean length1.1363636
Min length1

Characters and Unicode

Total characters2625
Distinct characters6
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 2100
90.9%
<NA> 105
 
4.5%
2 105
 
4.5%

Length

2023-10-20T16:27:36.185894image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-20T16:27:36.348526image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
1 2100
90.9%
na 105
 
4.5%
2 105
 
4.5%

Most occurring characters

ValueCountFrequency (%)
1 2100
80.0%
< 105
 
4.0%
N 105
 
4.0%
A 105
 
4.0%
> 105
 
4.0%
2 105
 
4.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2205
84.0%
Math Symbol 210
 
8.0%
Uppercase Letter 210
 
8.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 2100
95.2%
2 105
 
4.8%
Math Symbol
ValueCountFrequency (%)
< 105
50.0%
> 105
50.0%
Uppercase Letter
ValueCountFrequency (%)
N 105
50.0%
A 105
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2415
92.0%
Latin 210
 
8.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 2100
87.0%
< 105
 
4.3%
> 105
 
4.3%
2 105
 
4.3%
Latin
ValueCountFrequency (%)
N 105
50.0%
A 105
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2625
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 2100
80.0%
< 105
 
4.0%
N 105
 
4.0%
A 105
 
4.0%
> 105
 
4.0%
2 105
 
4.0%

post_csq13
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size100.6 KiB
1
2100 
<NA>
 
105
2
 
105

Length

Max length4
Median length1
Mean length1.1363636
Min length1

Characters and Unicode

Total characters2625
Distinct characters6
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 2100
90.9%
<NA> 105
 
4.5%
2 105
 
4.5%

Length

2023-10-20T16:27:36.485814image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-20T16:27:36.651023image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
1 2100
90.9%
na 105
 
4.5%
2 105
 
4.5%

Most occurring characters

ValueCountFrequency (%)
1 2100
80.0%
< 105
 
4.0%
N 105
 
4.0%
A 105
 
4.0%
> 105
 
4.0%
2 105
 
4.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2205
84.0%
Math Symbol 210
 
8.0%
Uppercase Letter 210
 
8.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 2100
95.2%
2 105
 
4.8%
Math Symbol
ValueCountFrequency (%)
< 105
50.0%
> 105
50.0%
Uppercase Letter
ValueCountFrequency (%)
N 105
50.0%
A 105
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2415
92.0%
Latin 210
 
8.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 2100
87.0%
< 105
 
4.3%
> 105
 
4.3%
2 105
 
4.3%
Latin
ValueCountFrequency (%)
N 105
50.0%
A 105
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2625
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 2100
80.0%
< 105
 
4.0%
N 105
 
4.0%
A 105
 
4.0%
> 105
 
4.0%
2 105
 
4.0%

post_csq14
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size100.6 KiB
1
1995 
<NA>
 
105
2
 
105
3
 
105

Length

Max length4
Median length1
Mean length1.1363636
Min length1

Characters and Unicode

Total characters2625
Distinct characters7
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 1995
86.4%
<NA> 105
 
4.5%
2 105
 
4.5%
3 105
 
4.5%

Length

2023-10-20T16:27:36.791776image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-20T16:27:36.962500image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
1 1995
86.4%
na 105
 
4.5%
2 105
 
4.5%
3 105
 
4.5%

Most occurring characters

ValueCountFrequency (%)
1 1995
76.0%
< 105
 
4.0%
N 105
 
4.0%
A 105
 
4.0%
> 105
 
4.0%
2 105
 
4.0%
3 105
 
4.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2205
84.0%
Math Symbol 210
 
8.0%
Uppercase Letter 210
 
8.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1995
90.5%
2 105
 
4.8%
3 105
 
4.8%
Math Symbol
ValueCountFrequency (%)
< 105
50.0%
> 105
50.0%
Uppercase Letter
ValueCountFrequency (%)
N 105
50.0%
A 105
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2415
92.0%
Latin 210
 
8.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 1995
82.6%
< 105
 
4.3%
> 105
 
4.3%
2 105
 
4.3%
3 105
 
4.3%
Latin
ValueCountFrequency (%)
N 105
50.0%
A 105
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2625
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 1995
76.0%
< 105
 
4.0%
N 105
 
4.0%
A 105
 
4.0%
> 105
 
4.0%
2 105
 
4.0%
3 105
 
4.0%

post_csq15
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size100.6 KiB
1
1470 
2
525 
7
 
105
<NA>
 
105
3
 
105

Length

Max length4
Median length1
Mean length1.1363636
Min length1

Characters and Unicode

Total characters2625
Distinct characters8
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 1470
63.6%
2 525
 
22.7%
7 105
 
4.5%
<NA> 105
 
4.5%
3 105
 
4.5%

Length

2023-10-20T16:27:37.105782image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-20T16:27:37.279545image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
1 1470
63.6%
2 525
 
22.7%
7 105
 
4.5%
na 105
 
4.5%
3 105
 
4.5%

Most occurring characters

ValueCountFrequency (%)
1 1470
56.0%
2 525
 
20.0%
7 105
 
4.0%
< 105
 
4.0%
N 105
 
4.0%
A 105
 
4.0%
> 105
 
4.0%
3 105
 
4.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2205
84.0%
Math Symbol 210
 
8.0%
Uppercase Letter 210
 
8.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1470
66.7%
2 525
 
23.8%
7 105
 
4.8%
3 105
 
4.8%
Math Symbol
ValueCountFrequency (%)
< 105
50.0%
> 105
50.0%
Uppercase Letter
ValueCountFrequency (%)
N 105
50.0%
A 105
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2415
92.0%
Latin 210
 
8.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 1470
60.9%
2 525
 
21.7%
7 105
 
4.3%
< 105
 
4.3%
> 105
 
4.3%
3 105
 
4.3%
Latin
ValueCountFrequency (%)
N 105
50.0%
A 105
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2625
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 1470
56.0%
2 525
 
20.0%
7 105
 
4.0%
< 105
 
4.0%
N 105
 
4.0%
A 105
 
4.0%
> 105
 
4.0%
3 105
 
4.0%

post_csq16
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size100.6 KiB
1
2100 
<NA>
 
105
2
 
105

Length

Max length4
Median length1
Mean length1.1363636
Min length1

Characters and Unicode

Total characters2625
Distinct characters6
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 2100
90.9%
<NA> 105
 
4.5%
2 105
 
4.5%

Length

2023-10-20T16:27:37.427706image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-20T16:27:37.597440image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
1 2100
90.9%
na 105
 
4.5%
2 105
 
4.5%

Most occurring characters

ValueCountFrequency (%)
1 2100
80.0%
< 105
 
4.0%
N 105
 
4.0%
A 105
 
4.0%
> 105
 
4.0%
2 105
 
4.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2205
84.0%
Math Symbol 210
 
8.0%
Uppercase Letter 210
 
8.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 2100
95.2%
2 105
 
4.8%
Math Symbol
ValueCountFrequency (%)
< 105
50.0%
> 105
50.0%
Uppercase Letter
ValueCountFrequency (%)
N 105
50.0%
A 105
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2415
92.0%
Latin 210
 
8.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 2100
87.0%
< 105
 
4.3%
> 105
 
4.3%
2 105
 
4.3%
Latin
ValueCountFrequency (%)
N 105
50.0%
A 105
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2625
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 2100
80.0%
< 105
 
4.0%
N 105
 
4.0%
A 105
 
4.0%
> 105
 
4.0%
2 105
 
4.0%

EHQ1
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size100.6 KiB
4
2205 
3
 
105

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2310
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4
2nd row4
3rd row4
4th row4
5th row4

Common Values

ValueCountFrequency (%)
4 2205
95.5%
3 105
 
4.5%

Length

2023-10-20T16:27:37.729963image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-20T16:27:37.881209image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
4 2205
95.5%
3 105
 
4.5%

Most occurring characters

ValueCountFrequency (%)
4 2205
95.5%
3 105
 
4.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2310
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 2205
95.5%
3 105
 
4.5%

Most occurring scripts

ValueCountFrequency (%)
Common 2310
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
4 2205
95.5%
3 105
 
4.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2310
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4 2205
95.5%
3 105
 
4.5%

EHQ2
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size100.6 KiB
4
1995 
3
210 
5
 
105

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2310
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row5
2nd row5
3rd row5
4th row5
5th row5

Common Values

ValueCountFrequency (%)
4 1995
86.4%
3 210
 
9.1%
5 105
 
4.5%

Length

2023-10-20T16:27:38.003013image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-20T16:27:38.155845image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
4 1995
86.4%
3 210
 
9.1%
5 105
 
4.5%

Most occurring characters

ValueCountFrequency (%)
4 1995
86.4%
3 210
 
9.1%
5 105
 
4.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2310
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 1995
86.4%
3 210
 
9.1%
5 105
 
4.5%

Most occurring scripts

ValueCountFrequency (%)
Common 2310
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
4 1995
86.4%
3 210
 
9.1%
5 105
 
4.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2310
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4 1995
86.4%
3 210
 
9.1%
5 105
 
4.5%

EHQ3
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size100.6 KiB
3
1155 
4
1050 
5
 
105

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2310
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row5
2nd row5
3rd row5
4th row5
5th row5

Common Values

ValueCountFrequency (%)
3 1155
50.0%
4 1050
45.5%
5 105
 
4.5%

Length

2023-10-20T16:27:38.287847image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-20T16:27:38.442937image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
3 1155
50.0%
4 1050
45.5%
5 105
 
4.5%

Most occurring characters

ValueCountFrequency (%)
3 1155
50.0%
4 1050
45.5%
5 105
 
4.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2310
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 1155
50.0%
4 1050
45.5%
5 105
 
4.5%

Most occurring scripts

ValueCountFrequency (%)
Common 2310
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 1155
50.0%
4 1050
45.5%
5 105
 
4.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2310
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 1155
50.0%
4 1050
45.5%
5 105
 
4.5%

EHQ4
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size100.6 KiB
4
1680 
3
630 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2310
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4
2nd row4
3rd row4
4th row4
5th row4

Common Values

ValueCountFrequency (%)
4 1680
72.7%
3 630
 
27.3%

Length

2023-10-20T16:27:38.579928image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-20T16:27:38.737814image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
4 1680
72.7%
3 630
 
27.3%

Most occurring characters

ValueCountFrequency (%)
4 1680
72.7%
3 630
 
27.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2310
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 1680
72.7%
3 630
 
27.3%

Most occurring scripts

ValueCountFrequency (%)
Common 2310
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
4 1680
72.7%
3 630
 
27.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2310
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4 1680
72.7%
3 630
 
27.3%

EHQ5
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size100.6 KiB
4
1260 
3
945 
5
 
105

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2310
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4
2nd row4
3rd row4
4th row4
5th row4

Common Values

ValueCountFrequency (%)
4 1260
54.5%
3 945
40.9%
5 105
 
4.5%

Length

2023-10-20T16:27:38.865797image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-20T16:27:39.022113image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
4 1260
54.5%
3 945
40.9%
5 105
 
4.5%

Most occurring characters

ValueCountFrequency (%)
4 1260
54.5%
3 945
40.9%
5 105
 
4.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2310
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 1260
54.5%
3 945
40.9%
5 105
 
4.5%

Most occurring scripts

ValueCountFrequency (%)
Common 2310
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
4 1260
54.5%
3 945
40.9%
5 105
 
4.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2310
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4 1260
54.5%
3 945
40.9%
5 105
 
4.5%

EHQ6
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size100.6 KiB
4
1365 
3
735 
5
210 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2310
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row5
2nd row5
3rd row5
4th row5
5th row5

Common Values

ValueCountFrequency (%)
4 1365
59.1%
3 735
31.8%
5 210
 
9.1%

Length

2023-10-20T16:27:39.162554image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-20T16:27:39.318405image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
4 1365
59.1%
3 735
31.8%
5 210
 
9.1%

Most occurring characters

ValueCountFrequency (%)
4 1365
59.1%
3 735
31.8%
5 210
 
9.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2310
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 1365
59.1%
3 735
31.8%
5 210
 
9.1%

Most occurring scripts

ValueCountFrequency (%)
Common 2310
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
4 1365
59.1%
3 735
31.8%
5 210
 
9.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2310
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4 1365
59.1%
3 735
31.8%
5 210
 
9.1%

EHQ7
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size100.6 KiB
3
1155 
4
1050 
5
 
105

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2310
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4
2nd row4
3rd row4
4th row4
5th row4

Common Values

ValueCountFrequency (%)
3 1155
50.0%
4 1050
45.5%
5 105
 
4.5%

Length

2023-10-20T16:27:39.456085image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-20T16:27:39.613834image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
3 1155
50.0%
4 1050
45.5%
5 105
 
4.5%

Most occurring characters

ValueCountFrequency (%)
3 1155
50.0%
4 1050
45.5%
5 105
 
4.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2310
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 1155
50.0%
4 1050
45.5%
5 105
 
4.5%

Most occurring scripts

ValueCountFrequency (%)
Common 2310
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 1155
50.0%
4 1050
45.5%
5 105
 
4.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2310
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 1155
50.0%
4 1050
45.5%
5 105
 
4.5%

EHQ8
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size100.6 KiB
3
840 
5
630 
4
525 
1
210 
2
105 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2310
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row5
2nd row5
3rd row5
4th row5
5th row5

Common Values

ValueCountFrequency (%)
3 840
36.4%
5 630
27.3%
4 525
22.7%
1 210
 
9.1%
2 105
 
4.5%

Length

2023-10-20T16:27:39.747095image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-20T16:27:39.912963image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
3 840
36.4%
5 630
27.3%
4 525
22.7%
1 210
 
9.1%
2 105
 
4.5%

Most occurring characters

ValueCountFrequency (%)
3 840
36.4%
5 630
27.3%
4 525
22.7%
1 210
 
9.1%
2 105
 
4.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2310
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 840
36.4%
5 630
27.3%
4 525
22.7%
1 210
 
9.1%
2 105
 
4.5%

Most occurring scripts

ValueCountFrequency (%)
Common 2310
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 840
36.4%
5 630
27.3%
4 525
22.7%
1 210
 
9.1%
2 105
 
4.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2310
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 840
36.4%
5 630
27.3%
4 525
22.7%
1 210
 
9.1%
2 105
 
4.5%

EHQ9
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size100.6 KiB
4
1470 
3
735 
5
 
105

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2310
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row5
2nd row5
3rd row5
4th row5
5th row5

Common Values

ValueCountFrequency (%)
4 1470
63.6%
3 735
31.8%
5 105
 
4.5%

Length

2023-10-20T16:27:40.065418image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-20T16:27:40.220258image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
4 1470
63.6%
3 735
31.8%
5 105
 
4.5%

Most occurring characters

ValueCountFrequency (%)
4 1470
63.6%
3 735
31.8%
5 105
 
4.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2310
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 1470
63.6%
3 735
31.8%
5 105
 
4.5%

Most occurring scripts

ValueCountFrequency (%)
Common 2310
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
4 1470
63.6%
3 735
31.8%
5 105
 
4.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2310
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4 1470
63.6%
3 735
31.8%
5 105
 
4.5%

EHQ10
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size100.6 KiB
3
1260 
4
525 
5
315 
2
 
105
1
 
105

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2310
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row5
2nd row5
3rd row5
4th row5
5th row5

Common Values

ValueCountFrequency (%)
3 1260
54.5%
4 525
22.7%
5 315
 
13.6%
2 105
 
4.5%
1 105
 
4.5%

Length

2023-10-20T16:27:40.354435image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-20T16:27:40.521091image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
3 1260
54.5%
4 525
22.7%
5 315
 
13.6%
2 105
 
4.5%
1 105
 
4.5%

Most occurring characters

ValueCountFrequency (%)
3 1260
54.5%
4 525
22.7%
5 315
 
13.6%
2 105
 
4.5%
1 105
 
4.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2310
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 1260
54.5%
4 525
22.7%
5 315
 
13.6%
2 105
 
4.5%
1 105
 
4.5%

Most occurring scripts

ValueCountFrequency (%)
Common 2310
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 1260
54.5%
4 525
22.7%
5 315
 
13.6%
2 105
 
4.5%
1 105
 
4.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2310
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 1260
54.5%
4 525
22.7%
5 315
 
13.6%
2 105
 
4.5%
1 105
 
4.5%

EHQI
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size100.6 KiB
3
1575 
4
735 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2310
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row3
3rd row3
4th row3
5th row3

Common Values

ValueCountFrequency (%)
3 1575
68.2%
4 735
31.8%

Length

2023-10-20T16:27:40.675387image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-20T16:27:40.833316image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
3 1575
68.2%
4 735
31.8%

Most occurring characters

ValueCountFrequency (%)
3 1575
68.2%
4 735
31.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2310
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 1575
68.2%
4 735
31.8%

Most occurring scripts

ValueCountFrequency (%)
Common 2310
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 1575
68.2%
4 735
31.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2310
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 1575
68.2%
4 735
31.8%

EHQII
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size100.6 KiB
3
735 
2
735 
4
420 
1
315 
5
105 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2310
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row3
3rd row3
4th row3
5th row3

Common Values

ValueCountFrequency (%)
3 735
31.8%
2 735
31.8%
4 420
18.2%
1 315
13.6%
5 105
 
4.5%

Length

2023-10-20T16:27:40.969246image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-20T16:27:41.146862image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
3 735
31.8%
2 735
31.8%
4 420
18.2%
1 315
13.6%
5 105
 
4.5%

Most occurring characters

ValueCountFrequency (%)
3 735
31.8%
2 735
31.8%
4 420
18.2%
1 315
13.6%
5 105
 
4.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2310
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 735
31.8%
2 735
31.8%
4 420
18.2%
1 315
13.6%
5 105
 
4.5%

Most occurring scripts

ValueCountFrequency (%)
Common 2310
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 735
31.8%
2 735
31.8%
4 420
18.2%
1 315
13.6%
5 105
 
4.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2310
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 735
31.8%
2 735
31.8%
4 420
18.2%
1 315
13.6%
5 105
 
4.5%

EHQ_F
Categorical

HIGH CORRELATION  MISSING 

Distinct8
Distinct (%)0.5%
Missing840
Missing (%)36.4%
Memory size100.6 KiB
7
420 
2
315 
nein
210 
4
105 
keine
105 
Other values (3)
315 

Length

Max length5
Median length1
Mean length2
Min length1

Characters and Unicode

Total characters2940
Distinct characters10
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
7 420
18.2%
2 315
 
13.6%
nein 210
 
9.1%
4 105
 
4.5%
keine 105
 
4.5%
1,2 105
 
4.5%
105
 
4.5%
2,4 105
 
4.5%
(Missing) 840
36.4%

Length

2023-10-20T16:27:41.309147image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-20T16:27:41.517769image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
7 420
30.8%
2 315
23.1%
nein 210
15.4%
4 105
 
7.7%
keine 105
 
7.7%
1,2 105
 
7.7%
2,4 105
 
7.7%

Most occurring characters

ValueCountFrequency (%)
2 525
17.9%
n 525
17.9%
7 420
14.3%
e 420
14.3%
i 315
10.7%
4 210
 
7.1%
, 210
 
7.1%
k 105
 
3.6%
1 105
 
3.6%
105
 
3.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1365
46.4%
Decimal Number 1260
42.9%
Other Punctuation 210
 
7.1%
Space Separator 105
 
3.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 525
41.7%
7 420
33.3%
4 210
 
16.7%
1 105
 
8.3%
Lowercase Letter
ValueCountFrequency (%)
n 525
38.5%
e 420
30.8%
i 315
23.1%
k 105
 
7.7%
Other Punctuation
ValueCountFrequency (%)
, 210
100.0%
Space Separator
ValueCountFrequency (%)
105
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1575
53.6%
Latin 1365
46.4%

Most frequent character per script

Common
ValueCountFrequency (%)
2 525
33.3%
7 420
26.7%
4 210
 
13.3%
, 210
 
13.3%
1 105
 
6.7%
105
 
6.7%
Latin
ValueCountFrequency (%)
n 525
38.5%
e 420
30.8%
i 315
23.1%
k 105
 
7.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2940
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 525
17.9%
n 525
17.9%
7 420
14.3%
e 420
14.3%
i 315
10.7%
4 210
 
7.1%
, 210
 
7.1%
k 105
 
3.6%
1 105
 
3.6%
105
 
3.6%

post_VRF1
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct7
Distinct (%)0.3%
Missing105
Missing (%)4.5%
Infinite0
Infinite (%)0.0%
Mean4.1428571
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size100.6 KiB
2023-10-20T16:27:41.678089image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q13
median4
Q35
95-th percentile6
Maximum7
Range6
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.5211754
Coefficient of variation (CV)0.36718027
Kurtosis-0.73950961
Mean4.1428571
Median Absolute Deviation (MAD)1
Skewness-0.16090177
Sum9135
Variance2.3139746
MonotonicityNot monotonic
2023-10-20T16:27:41.812990image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
5 630
27.3%
3 525
22.7%
4 315
13.6%
6 315
13.6%
2 210
 
9.1%
7 105
 
4.5%
1 105
 
4.5%
(Missing) 105
 
4.5%
ValueCountFrequency (%)
1 105
 
4.5%
2 210
 
9.1%
3 525
22.7%
4 315
13.6%
5 630
27.3%
6 315
13.6%
7 105
 
4.5%
ValueCountFrequency (%)
7 105
 
4.5%
6 315
13.6%
5 630
27.3%
4 315
13.6%
3 525
22.7%
2 210
 
9.1%
1 105
 
4.5%

post_VRF2
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct6
Distinct (%)0.3%
Missing105
Missing (%)4.5%
Infinite0
Infinite (%)0.0%
Mean2.7619048
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size100.6 KiB
2023-10-20T16:27:41.957695image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median2
Q33
95-th percentile6
Maximum6
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.4446849
Coefficient of variation (CV)0.52307555
Kurtosis0.084376091
Mean2.7619048
Median Absolute Deviation (MAD)1
Skewness0.98960069
Sum6090
Variance2.0871143
MonotonicityNot monotonic
2023-10-20T16:27:42.113219image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
2 945
40.9%
3 420
18.2%
1 315
 
13.6%
6 210
 
9.1%
4 210
 
9.1%
5 105
 
4.5%
(Missing) 105
 
4.5%
ValueCountFrequency (%)
1 315
 
13.6%
2 945
40.9%
3 420
18.2%
4 210
 
9.1%
5 105
 
4.5%
6 210
 
9.1%
ValueCountFrequency (%)
6 210
 
9.1%
5 105
 
4.5%
4 210
 
9.1%
3 420
18.2%
2 945
40.9%
1 315
 
13.6%

post_VRF3
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size100.6 KiB
6
840 
5
525 
7
420 
4
420 
<NA>
105 

Length

Max length4
Median length1
Mean length1.1363636
Min length1

Characters and Unicode

Total characters2625
Distinct characters8
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row5
2nd row5
3rd row5
4th row5
5th row5

Common Values

ValueCountFrequency (%)
6 840
36.4%
5 525
22.7%
7 420
18.2%
4 420
18.2%
<NA> 105
 
4.5%

Length

2023-10-20T16:27:42.280400image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-20T16:27:42.459637image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
6 840
36.4%
5 525
22.7%
7 420
18.2%
4 420
18.2%
na 105
 
4.5%

Most occurring characters

ValueCountFrequency (%)
6 840
32.0%
5 525
20.0%
7 420
16.0%
4 420
16.0%
< 105
 
4.0%
N 105
 
4.0%
A 105
 
4.0%
> 105
 
4.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2205
84.0%
Math Symbol 210
 
8.0%
Uppercase Letter 210
 
8.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
6 840
38.1%
5 525
23.8%
7 420
19.0%
4 420
19.0%
Math Symbol
ValueCountFrequency (%)
< 105
50.0%
> 105
50.0%
Uppercase Letter
ValueCountFrequency (%)
N 105
50.0%
A 105
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2415
92.0%
Latin 210
 
8.0%

Most frequent character per script

Common
ValueCountFrequency (%)
6 840
34.8%
5 525
21.7%
7 420
17.4%
4 420
17.4%
< 105
 
4.3%
> 105
 
4.3%
Latin
ValueCountFrequency (%)
N 105
50.0%
A 105
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2625
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
6 840
32.0%
5 525
20.0%
7 420
16.0%
4 420
16.0%
< 105
 
4.0%
N 105
 
4.0%
A 105
 
4.0%
> 105
 
4.0%

post_VRF4
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct7
Distinct (%)0.3%
Missing105
Missing (%)4.5%
Infinite0
Infinite (%)0.0%
Mean4.5238095
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size102.9 KiB
2023-10-20T16:27:42.598903image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q14
median5
Q36
95-th percentile7
Maximum7
Range6
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.6222142
Coefficient of variation (CV)0.35859472
Kurtosis-0.61990074
Mean4.5238095
Median Absolute Deviation (MAD)1
Skewness-0.41128337
Sum9975
Variance2.6315789
MonotonicityNot monotonic
2023-10-20T16:27:42.738235image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
4 525
22.7%
6 525
22.7%
5 420
18.2%
2 210
 
9.1%
7 210
 
9.1%
3 210
 
9.1%
1 105
 
4.5%
(Missing) 105
 
4.5%
ValueCountFrequency (%)
1 105
 
4.5%
2 210
 
9.1%
3 210
 
9.1%
4 525
22.7%
5 420
18.2%
6 525
22.7%
7 210
 
9.1%
ValueCountFrequency (%)
7 210
 
9.1%
6 525
22.7%
5 420
18.2%
4 525
22.7%
3 210
 
9.1%
2 210
 
9.1%
1 105
 
4.5%

post_VRF5
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct7
Distinct (%)0.3%
Missing105
Missing (%)4.5%
Infinite0
Infinite (%)0.0%
Mean3.8095238
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size102.9 KiB
2023-10-20T16:27:42.888848image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q36
95-th percentile7
Maximum7
Range6
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.0151423
Coefficient of variation (CV)0.52897486
Kurtosis-1.228218
Mean3.8095238
Median Absolute Deviation (MAD)2
Skewness0.22639916
Sum8400
Variance4.0607985
MonotonicityNot monotonic
2023-10-20T16:27:43.021792image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
2 420
18.2%
4 420
18.2%
6 315
13.6%
3 315
13.6%
1 315
13.6%
7 315
13.6%
5 105
 
4.5%
(Missing) 105
 
4.5%
ValueCountFrequency (%)
1 315
13.6%
2 420
18.2%
3 315
13.6%
4 420
18.2%
5 105
 
4.5%
6 315
13.6%
7 315
13.6%
ValueCountFrequency (%)
7 315
13.6%
6 315
13.6%
5 105
 
4.5%
4 420
18.2%
3 315
13.6%
2 420
18.2%
1 315
13.6%

post_VRF6
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct6
Distinct (%)0.3%
Missing105
Missing (%)4.5%
Infinite0
Infinite (%)0.0%
Mean4.047619
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size102.9 KiB
2023-10-20T16:27:43.161762image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median5
Q35
95-th percentile6
Maximum6
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.4956076
Coefficient of variation (CV)0.36950306
Kurtosis-0.63066213
Mean4.047619
Median Absolute Deviation (MAD)1
Skewness-0.76554716
Sum8925
Variance2.2368421
MonotonicityNot monotonic
2023-10-20T16:27:43.309082image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
5 1050
45.5%
3 315
 
13.6%
1 210
 
9.1%
4 210
 
9.1%
6 210
 
9.1%
2 210
 
9.1%
(Missing) 105
 
4.5%
ValueCountFrequency (%)
1 210
 
9.1%
2 210
 
9.1%
3 315
 
13.6%
4 210
 
9.1%
5 1050
45.5%
6 210
 
9.1%
ValueCountFrequency (%)
6 210
 
9.1%
5 1050
45.5%
4 210
 
9.1%
3 315
 
13.6%
2 210
 
9.1%
1 210
 
9.1%

post_VRF7
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct7
Distinct (%)0.3%
Missing105
Missing (%)4.5%
Infinite0
Infinite (%)0.0%
Mean4.4285714
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size102.9 KiB
2023-10-20T16:27:43.449001image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q13
median4
Q36
95-th percentile7
Maximum7
Range6
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.7616607
Coefficient of variation (CV)0.39779434
Kurtosis-1.0878687
Mean4.4285714
Median Absolute Deviation (MAD)2
Skewness-0.090777691
Sum9765
Variance3.1034483
MonotonicityNot monotonic
2023-10-20T16:27:43.582882image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
6 525
22.7%
4 525
22.7%
3 420
18.2%
7 315
13.6%
2 210
 
9.1%
1 105
 
4.5%
5 105
 
4.5%
(Missing) 105
 
4.5%
ValueCountFrequency (%)
1 105
 
4.5%
2 210
 
9.1%
3 420
18.2%
4 525
22.7%
5 105
 
4.5%
6 525
22.7%
7 315
13.6%
ValueCountFrequency (%)
7 315
13.6%
6 525
22.7%
5 105
 
4.5%
4 525
22.7%
3 420
18.2%
2 210
 
9.1%
1 105
 
4.5%

post_VRF8
Categorical

HIGH CORRELATION 

Distinct6
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size100.6 KiB
6
945 
5
525 
4
315 
2
210 
7
210 

Length

Max length4
Median length1
Mean length1.1363636
Min length1

Characters and Unicode

Total characters2625
Distinct characters9
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row6
2nd row6
3rd row6
4th row6
5th row6

Common Values

ValueCountFrequency (%)
6 945
40.9%
5 525
22.7%
4 315
 
13.6%
2 210
 
9.1%
7 210
 
9.1%
<NA> 105
 
4.5%

Length

2023-10-20T16:27:43.751720image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-20T16:27:43.935634image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
6 945
40.9%
5 525
22.7%
4 315
 
13.6%
2 210
 
9.1%
7 210
 
9.1%
na 105
 
4.5%

Most occurring characters

ValueCountFrequency (%)
6 945
36.0%
5 525
20.0%
4 315
 
12.0%
2 210
 
8.0%
7 210
 
8.0%
< 105
 
4.0%
N 105
 
4.0%
A 105
 
4.0%
> 105
 
4.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2205
84.0%
Math Symbol 210
 
8.0%
Uppercase Letter 210
 
8.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
6 945
42.9%
5 525
23.8%
4 315
 
14.3%
2 210
 
9.5%
7 210
 
9.5%
Math Symbol
ValueCountFrequency (%)
< 105
50.0%
> 105
50.0%
Uppercase Letter
ValueCountFrequency (%)
N 105
50.0%
A 105
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2415
92.0%
Latin 210
 
8.0%

Most frequent character per script

Common
ValueCountFrequency (%)
6 945
39.1%
5 525
21.7%
4 315
 
13.0%
2 210
 
8.7%
7 210
 
8.7%
< 105
 
4.3%
> 105
 
4.3%
Latin
ValueCountFrequency (%)
N 105
50.0%
A 105
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2625
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
6 945
36.0%
5 525
20.0%
4 315
 
12.0%
2 210
 
8.0%
7 210
 
8.0%
< 105
 
4.0%
N 105
 
4.0%
A 105
 
4.0%
> 105
 
4.0%

post_VRF9
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct6
Distinct (%)0.3%
Missing105
Missing (%)4.5%
Infinite0
Infinite (%)0.0%
Mean4.2857143
Minimum2
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size102.9 KiB
2023-10-20T16:27:44.080827image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile3
Q14
median4
Q35
95-th percentile6
Maximum7
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.1190481
Coefficient of variation (CV)0.26111122
Kurtosis0.13381232
Mean4.2857143
Median Absolute Deviation (MAD)1
Skewness0.23749561
Sum9450
Variance1.2522686
MonotonicityNot monotonic
2023-10-20T16:27:44.236123image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
4 735
31.8%
5 735
31.8%
3 420
18.2%
6 105
 
4.5%
2 105
 
4.5%
7 105
 
4.5%
(Missing) 105
 
4.5%
ValueCountFrequency (%)
2 105
 
4.5%
3 420
18.2%
4 735
31.8%
5 735
31.8%
6 105
 
4.5%
7 105
 
4.5%
ValueCountFrequency (%)
7 105
 
4.5%
6 105
 
4.5%
5 735
31.8%
4 735
31.8%
3 420
18.2%
2 105
 
4.5%

post_VRF10
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct6
Distinct (%)0.3%
Missing105
Missing (%)4.5%
Infinite0
Infinite (%)0.0%
Mean4.6666667
Minimum2
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size102.9 KiB
2023-10-20T16:27:44.383680image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile2
Q14
median5
Q36
95-th percentile6
Maximum7
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.3572418
Coefficient of variation (CV)0.29083753
Kurtosis-0.60775371
Mean4.6666667
Median Absolute Deviation (MAD)1
Skewness-0.52554428
Sum10290
Variance1.8421053
MonotonicityNot monotonic
2023-10-20T16:27:44.527540image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
5 840
36.4%
6 525
22.7%
3 315
 
13.6%
2 210
 
9.1%
4 210
 
9.1%
7 105
 
4.5%
(Missing) 105
 
4.5%
ValueCountFrequency (%)
2 210
 
9.1%
3 315
 
13.6%
4 210
 
9.1%
5 840
36.4%
6 525
22.7%
7 105
 
4.5%
ValueCountFrequency (%)
7 105
 
4.5%
6 525
22.7%
5 840
36.4%
4 210
 
9.1%
3 315
 
13.6%
2 210
 
9.1%

post_VRF11
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct6
Distinct (%)0.3%
Missing105
Missing (%)4.5%
Infinite0
Infinite (%)0.0%
Mean3.1428571
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size102.9 KiB
2023-10-20T16:27:44.666021image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q34
95-th percentile5
Maximum6
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.4571932
Coefficient of variation (CV)0.46365238
Kurtosis-1.0162251
Mean3.1428571
Median Absolute Deviation (MAD)1
Skewness0.21323281
Sum6930
Variance2.123412
MonotonicityNot monotonic
2023-10-20T16:27:44.813122image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
2 525
22.7%
3 525
22.7%
5 420
18.2%
4 315
13.6%
1 315
13.6%
6 105
 
4.5%
(Missing) 105
 
4.5%
ValueCountFrequency (%)
1 315
13.6%
2 525
22.7%
3 525
22.7%
4 315
13.6%
5 420
18.2%
6 105
 
4.5%
ValueCountFrequency (%)
6 105
 
4.5%
5 420
18.2%
4 315
13.6%
3 525
22.7%
2 525
22.7%
1 315
13.6%

post_VRF12
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct7
Distinct (%)0.3%
Missing105
Missing (%)4.5%
Infinite0
Infinite (%)0.0%
Mean3.7619048
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size102.9 KiB
2023-10-20T16:27:44.956421image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median3
Q35
95-th percentile6
Maximum7
Range6
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.6880062
Coefficient of variation (CV)0.4487105
Kurtosis-0.99223122
Mean3.7619048
Median Absolute Deviation (MAD)1
Skewness0.13884122
Sum8295
Variance2.8493648
MonotonicityNot monotonic
2023-10-20T16:27:45.095943image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
3 630
27.3%
5 420
18.2%
6 315
13.6%
2 315
13.6%
4 210
 
9.1%
1 210
 
9.1%
7 105
 
4.5%
(Missing) 105
 
4.5%
ValueCountFrequency (%)
1 210
 
9.1%
2 315
13.6%
3 630
27.3%
4 210
 
9.1%
5 420
18.2%
6 315
13.6%
7 105
 
4.5%
ValueCountFrequency (%)
7 105
 
4.5%
6 315
13.6%
5 420
18.2%
4 210
 
9.1%
3 630
27.3%
2 315
13.6%
1 210
 
9.1%

post_VRF13
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct7
Distinct (%)0.3%
Missing105
Missing (%)4.5%
Infinite0
Infinite (%)0.0%
Mean4.2380952
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size102.9 KiB
2023-10-20T16:27:45.247507image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median5
Q36
95-th percentile7
Maximum7
Range6
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.7706521
Coefficient of variation (CV)0.41779431
Kurtosis-0.98391654
Mean4.2380952
Median Absolute Deviation (MAD)2
Skewness-0.20864313
Sum9345
Variance3.1352087
MonotonicityNot monotonic
2023-10-20T16:27:45.380134image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
3 630
27.3%
5 525
22.7%
6 420
18.2%
7 210
 
9.1%
1 210
 
9.1%
4 105
 
4.5%
2 105
 
4.5%
(Missing) 105
 
4.5%
ValueCountFrequency (%)
1 210
 
9.1%
2 105
 
4.5%
3 630
27.3%
4 105
 
4.5%
5 525
22.7%
6 420
18.2%
7 210
 
9.1%
ValueCountFrequency (%)
7 210
 
9.1%
6 420
18.2%
5 525
22.7%
4 105
 
4.5%
3 630
27.3%
2 105
 
4.5%
1 210
 
9.1%

post_VRF14
Categorical

HIGH CORRELATION 

Distinct6
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size100.6 KiB
3
945 
1
525 
5
315 
4
315 
<NA>
105 

Length

Max length4
Median length1
Mean length1.1363636
Min length1

Characters and Unicode

Total characters2625
Distinct characters9
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row5
2nd row5
3rd row5
4th row5
5th row5

Common Values

ValueCountFrequency (%)
3 945
40.9%
1 525
22.7%
5 315
 
13.6%
4 315
 
13.6%
<NA> 105
 
4.5%
2 105
 
4.5%

Length

2023-10-20T16:27:45.550962image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-20T16:27:45.734742image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
3 945
40.9%
1 525
22.7%
5 315
 
13.6%
4 315
 
13.6%
na 105
 
4.5%
2 105
 
4.5%

Most occurring characters

ValueCountFrequency (%)
3 945
36.0%
1 525
20.0%
5 315
 
12.0%
4 315
 
12.0%
< 105
 
4.0%
N 105
 
4.0%
A 105
 
4.0%
> 105
 
4.0%
2 105
 
4.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2205
84.0%
Math Symbol 210
 
8.0%
Uppercase Letter 210
 
8.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 945
42.9%
1 525
23.8%
5 315
 
14.3%
4 315
 
14.3%
2 105
 
4.8%
Math Symbol
ValueCountFrequency (%)
< 105
50.0%
> 105
50.0%
Uppercase Letter
ValueCountFrequency (%)
N 105
50.0%
A 105
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2415
92.0%
Latin 210
 
8.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 945
39.1%
1 525
21.7%
5 315
 
13.0%
4 315
 
13.0%
< 105
 
4.3%
> 105
 
4.3%
2 105
 
4.3%
Latin
ValueCountFrequency (%)
N 105
50.0%
A 105
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2625
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 945
36.0%
1 525
20.0%
5 315
 
12.0%
4 315
 
12.0%
< 105
 
4.0%
N 105
 
4.0%
A 105
 
4.0%
> 105
 
4.0%
2 105
 
4.0%

post_VRF15
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct7
Distinct (%)0.3%
Missing105
Missing (%)4.5%
Infinite0
Infinite (%)0.0%
Mean3.7142857
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size102.9 KiB
2023-10-20T16:27:45.877989image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q13
median3
Q35
95-th percentile7
Maximum7
Range6
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.637525
Coefficient of variation (CV)0.44087212
Kurtosis-0.59915583
Mean3.7142857
Median Absolute Deviation (MAD)1
Skewness0.52923214
Sum8190
Variance2.6814882
MonotonicityNot monotonic
2023-10-20T16:27:46.013453image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
3 735
31.8%
2 420
18.2%
5 420
18.2%
7 210
 
9.1%
4 210
 
9.1%
6 105
 
4.5%
1 105
 
4.5%
(Missing) 105
 
4.5%
ValueCountFrequency (%)
1 105
 
4.5%
2 420
18.2%
3 735
31.8%
4 210
 
9.1%
5 420
18.2%
6 105
 
4.5%
7 210
 
9.1%
ValueCountFrequency (%)
7 210
 
9.1%
6 105
 
4.5%
5 420
18.2%
4 210
 
9.1%
3 735
31.8%
2 420
18.2%
1 105
 
4.5%

post_VRF16
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct6
Distinct (%)0.3%
Missing105
Missing (%)4.5%
Infinite0
Infinite (%)0.0%
Mean4.5714286
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size102.9 KiB
2023-10-20T16:27:46.167483image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median5
Q36
95-th percentile7
Maximum7
Range6
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.6785721
Coefficient of variation (CV)0.36718765
Kurtosis-0.22915841
Mean4.5714286
Median Absolute Deviation (MAD)1
Skewness-0.5172744
Sum10080
Variance2.8176044
MonotonicityNot monotonic
2023-10-20T16:27:46.305041image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
5 630
27.3%
4 420
18.2%
6 315
13.6%
3 315
13.6%
7 315
13.6%
1 210
 
9.1%
(Missing) 105
 
4.5%
ValueCountFrequency (%)
1 210
 
9.1%
3 315
13.6%
4 420
18.2%
5 630
27.3%
6 315
13.6%
7 315
13.6%
ValueCountFrequency (%)
7 315
13.6%
6 315
13.6%
5 630
27.3%
4 420
18.2%
3 315
13.6%
1 210
 
9.1%

post_VRF17
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct7
Distinct (%)0.3%
Missing105
Missing (%)4.5%
Infinite0
Infinite (%)0.0%
Mean4
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size102.9 KiB
2023-10-20T16:27:46.451138image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q36
95-th percentile6
Maximum7
Range6
Interquartile range (IQR)4

Descriptive statistics

Standard deviation1.826156
Coefficient of variation (CV)0.456539
Kurtosis-1.3116812
Mean4
Median Absolute Deviation (MAD)2
Skewness-0.14093884
Sum8820
Variance3.3348457
MonotonicityNot monotonic
2023-10-20T16:27:46.584295image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
6 525
22.7%
2 420
18.2%
5 420
18.2%
3 315
13.6%
4 210
 
9.1%
1 210
 
9.1%
7 105
 
4.5%
(Missing) 105
 
4.5%
ValueCountFrequency (%)
1 210
 
9.1%
2 420
18.2%
3 315
13.6%
4 210
 
9.1%
5 420
18.2%
6 525
22.7%
7 105
 
4.5%
ValueCountFrequency (%)
7 105
 
4.5%
6 525
22.7%
5 420
18.2%
4 210
 
9.1%
3 315
13.6%
2 420
18.2%
1 210
 
9.1%

post_VRF18
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct7
Distinct (%)0.3%
Missing105
Missing (%)4.5%
Infinite0
Infinite (%)0.0%
Mean4.2380952
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size102.9 KiB
2023-10-20T16:27:46.729627image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median4
Q36
95-th percentile7
Maximum7
Range6
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.9253302
Coefficient of variation (CV)0.45429141
Kurtosis-1.2090624
Mean4.2380952
Median Absolute Deviation (MAD)2
Skewness-0.13938157
Sum9345
Variance3.7068966
MonotonicityNot monotonic
2023-10-20T16:27:46.863758image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
6 420
18.2%
7 315
13.6%
2 315
13.6%
3 315
13.6%
5 315
13.6%
4 315
13.6%
1 210
9.1%
(Missing) 105
 
4.5%
ValueCountFrequency (%)
1 210
9.1%
2 315
13.6%
3 315
13.6%
4 315
13.6%
5 315
13.6%
6 420
18.2%
7 315
13.6%
ValueCountFrequency (%)
7 315
13.6%
6 420
18.2%
5 315
13.6%
4 315
13.6%
3 315
13.6%
2 315
13.6%
1 210
9.1%

post_VRF19
Categorical

HIGH CORRELATION 

Distinct6
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size100.6 KiB
6
945 
5
525 
7
315 
4
315 
<NA>
105 

Length

Max length4
Median length1
Mean length1.1363636
Min length1

Characters and Unicode

Total characters2625
Distinct characters9
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row6
2nd row6
3rd row6
4th row6
5th row6

Common Values

ValueCountFrequency (%)
6 945
40.9%
5 525
22.7%
7 315
 
13.6%
4 315
 
13.6%
<NA> 105
 
4.5%
3 105
 
4.5%

Length

2023-10-20T16:27:47.028622image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-20T16:27:47.209429image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
6 945
40.9%
5 525
22.7%
7 315
 
13.6%
4 315
 
13.6%
na 105
 
4.5%
3 105
 
4.5%

Most occurring characters

ValueCountFrequency (%)
6 945
36.0%
5 525
20.0%
7 315
 
12.0%
4 315
 
12.0%
< 105
 
4.0%
N 105
 
4.0%
A 105
 
4.0%
> 105
 
4.0%
3 105
 
4.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2205
84.0%
Math Symbol 210
 
8.0%
Uppercase Letter 210
 
8.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
6 945
42.9%
5 525
23.8%
7 315
 
14.3%
4 315
 
14.3%
3 105
 
4.8%
Math Symbol
ValueCountFrequency (%)
< 105
50.0%
> 105
50.0%
Uppercase Letter
ValueCountFrequency (%)
N 105
50.0%
A 105
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2415
92.0%
Latin 210
 
8.0%

Most frequent character per script

Common
ValueCountFrequency (%)
6 945
39.1%
5 525
21.7%
7 315
 
13.0%
4 315
 
13.0%
< 105
 
4.3%
> 105
 
4.3%
3 105
 
4.3%
Latin
ValueCountFrequency (%)
N 105
50.0%
A 105
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2625
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
6 945
36.0%
5 525
20.0%
7 315
 
12.0%
4 315
 
12.0%
< 105
 
4.0%
N 105
 
4.0%
A 105
 
4.0%
> 105
 
4.0%
3 105
 
4.0%

post_VRF20
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct6
Distinct (%)0.3%
Missing105
Missing (%)4.5%
Infinite0
Infinite (%)0.0%
Mean3.0952381
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size102.9 KiB
2023-10-20T16:27:47.348998image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q34
95-th percentile5
Maximum7
Range6
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.6305834
Coefficient of variation (CV)0.52680386
Kurtosis-0.39764719
Mean3.0952381
Median Absolute Deviation (MAD)1
Skewness0.44034848
Sum6825
Variance2.6588022
MonotonicityNot monotonic
2023-10-20T16:27:47.482594image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
3 735
31.8%
1 525
22.7%
5 420
18.2%
4 210
 
9.1%
2 210
 
9.1%
7 105
 
4.5%
(Missing) 105
 
4.5%
ValueCountFrequency (%)
1 525
22.7%
2 210
 
9.1%
3 735
31.8%
4 210
 
9.1%
5 420
18.2%
7 105
 
4.5%
ValueCountFrequency (%)
7 105
 
4.5%
5 420
18.2%
4 210
 
9.1%
3 735
31.8%
2 210
 
9.1%
1 525
22.7%

post_VRF21
Categorical

HIGH CORRELATION 

Distinct6
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size100.6 KiB
6
630 
3
525 
5
420 
4
315 
2
315 

Length

Max length4
Median length1
Mean length1.1363636
Min length1

Characters and Unicode

Total characters2625
Distinct characters9
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row3
3rd row3
4th row3
5th row3

Common Values

ValueCountFrequency (%)
6 630
27.3%
3 525
22.7%
5 420
18.2%
4 315
13.6%
2 315
13.6%
<NA> 105
 
4.5%

Length

2023-10-20T16:27:47.650866image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-20T16:27:47.834406image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
6 630
27.3%
3 525
22.7%
5 420
18.2%
4 315
13.6%
2 315
13.6%
na 105
 
4.5%

Most occurring characters

ValueCountFrequency (%)
6 630
24.0%
3 525
20.0%
5 420
16.0%
4 315
12.0%
2 315
12.0%
< 105
 
4.0%
N 105
 
4.0%
A 105
 
4.0%
> 105
 
4.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2205
84.0%
Math Symbol 210
 
8.0%
Uppercase Letter 210
 
8.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
6 630
28.6%
3 525
23.8%
5 420
19.0%
4 315
14.3%
2 315
14.3%
Math Symbol
ValueCountFrequency (%)
< 105
50.0%
> 105
50.0%
Uppercase Letter
ValueCountFrequency (%)
N 105
50.0%
A 105
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2415
92.0%
Latin 210
 
8.0%

Most frequent character per script

Common
ValueCountFrequency (%)
6 630
26.1%
3 525
21.7%
5 420
17.4%
4 315
13.0%
2 315
13.0%
< 105
 
4.3%
> 105
 
4.3%
Latin
ValueCountFrequency (%)
N 105
50.0%
A 105
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2625
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
6 630
24.0%
3 525
20.0%
5 420
16.0%
4 315
12.0%
2 315
12.0%
< 105
 
4.0%
N 105
 
4.0%
A 105
 
4.0%
> 105
 
4.0%

post_VRF22
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct6
Distinct (%)0.3%
Missing105
Missing (%)4.5%
Infinite0
Infinite (%)0.0%
Mean4.3333333
Minimum2
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size102.9 KiB
2023-10-20T16:27:47.977260image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile3
Q13
median4
Q35
95-th percentile6
Maximum7
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.247502
Coefficient of variation (CV)0.28788509
Kurtosis-0.632825
Mean4.3333333
Median Absolute Deviation (MAD)1
Skewness0.23469506
Sum9555
Variance1.5562613
MonotonicityNot monotonic
2023-10-20T16:27:48.124249image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
4 630
27.3%
3 525
22.7%
5 525
22.7%
6 315
13.6%
2 105
 
4.5%
7 105
 
4.5%
(Missing) 105
 
4.5%
ValueCountFrequency (%)
2 105
 
4.5%
3 525
22.7%
4 630
27.3%
5 525
22.7%
6 315
13.6%
7 105
 
4.5%
ValueCountFrequency (%)
7 105
 
4.5%
6 315
13.6%
5 525
22.7%
4 630
27.3%
3 525
22.7%
2 105
 
4.5%

post_VRF23
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct6
Distinct (%)0.3%
Missing105
Missing (%)4.5%
Infinite0
Infinite (%)0.0%
Mean4.7142857
Minimum2
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size102.9 KiB
2023-10-20T16:27:48.261309image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile2
Q14
median5
Q35
95-th percentile7
Maximum7
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.4525152
Coefficient of variation (CV)0.30810928
Kurtosis-0.61734719
Mean4.7142857
Median Absolute Deviation (MAD)1
Skewness-0.24197397
Sum10395
Variance2.1098004
MonotonicityNot monotonic
2023-10-20T16:27:48.404669image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
5 945
40.9%
7 315
 
13.6%
3 315
 
13.6%
4 210
 
9.1%
6 210
 
9.1%
2 210
 
9.1%
(Missing) 105
 
4.5%
ValueCountFrequency (%)
2 210
 
9.1%
3 315
 
13.6%
4 210
 
9.1%
5 945
40.9%
6 210
 
9.1%
7 315
 
13.6%
ValueCountFrequency (%)
7 315
 
13.6%
6 210
 
9.1%
5 945
40.9%
4 210
 
9.1%
3 315
 
13.6%
2 210
 
9.1%

post_VRF24
Categorical

HIGH CORRELATION 

Distinct6
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size100.6 KiB
6
1260 
7
420 
5
315 
3
 
105
<NA>
 
105

Length

Max length4
Median length1
Mean length1.1363636
Min length1

Characters and Unicode

Total characters2625
Distinct characters9
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row3
3rd row3
4th row3
5th row3

Common Values

ValueCountFrequency (%)
6 1260
54.5%
7 420
 
18.2%
5 315
 
13.6%
3 105
 
4.5%
<NA> 105
 
4.5%
4 105
 
4.5%

Length

2023-10-20T16:27:48.564722image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-20T16:27:48.746724image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
6 1260
54.5%
7 420
 
18.2%
5 315
 
13.6%
3 105
 
4.5%
na 105
 
4.5%
4 105
 
4.5%

Most occurring characters

ValueCountFrequency (%)
6 1260
48.0%
7 420
 
16.0%
5 315
 
12.0%
3 105
 
4.0%
< 105
 
4.0%
N 105
 
4.0%
A 105
 
4.0%
> 105
 
4.0%
4 105
 
4.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2205
84.0%
Math Symbol 210
 
8.0%
Uppercase Letter 210
 
8.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
6 1260
57.1%
7 420
 
19.0%
5 315
 
14.3%
3 105
 
4.8%
4 105
 
4.8%
Math Symbol
ValueCountFrequency (%)
< 105
50.0%
> 105
50.0%
Uppercase Letter
ValueCountFrequency (%)
N 105
50.0%
A 105
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2415
92.0%
Latin 210
 
8.0%

Most frequent character per script

Common
ValueCountFrequency (%)
6 1260
52.2%
7 420
 
17.4%
5 315
 
13.0%
3 105
 
4.3%
< 105
 
4.3%
> 105
 
4.3%
4 105
 
4.3%
Latin
ValueCountFrequency (%)
N 105
50.0%
A 105
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2625
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
6 1260
48.0%
7 420
 
16.0%
5 315
 
12.0%
3 105
 
4.0%
< 105
 
4.0%
N 105
 
4.0%
A 105
 
4.0%
> 105
 
4.0%
4 105
 
4.0%

post_VRF25
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct6
Distinct (%)0.3%
Missing105
Missing (%)4.5%
Infinite0
Infinite (%)0.0%
Mean3.5238095
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size102.9 KiB
2023-10-20T16:27:48.901834image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q13
median3
Q35
95-th percentile5
Maximum6
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.295672
Coefficient of variation (CV)0.3676907
Kurtosis-0.89995506
Mean3.5238095
Median Absolute Deviation (MAD)1
Skewness0.01332241
Sum7770
Variance1.6787659
MonotonicityNot monotonic
2023-10-20T16:27:49.063643image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
3 630
27.3%
5 525
22.7%
4 420
18.2%
2 420
18.2%
1 105
 
4.5%
6 105
 
4.5%
(Missing) 105
 
4.5%
ValueCountFrequency (%)
1 105
 
4.5%
2 420
18.2%
3 630
27.3%
4 420
18.2%
5 525
22.7%
6 105
 
4.5%
ValueCountFrequency (%)
6 105
 
4.5%
5 525
22.7%
4 420
18.2%
3 630
27.3%
2 420
18.2%
1 105
 
4.5%

post_VRF26
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct7
Distinct (%)0.3%
Missing105
Missing (%)4.5%
Infinite0
Infinite (%)0.0%
Mean3.5714286
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size102.9 KiB
2023-10-20T16:27:49.207527image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q13
median3
Q35
95-th percentile6
Maximum7
Range6
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.4336506
Coefficient of variation (CV)0.40142215
Kurtosis-0.14688277
Mean3.5714286
Median Absolute Deviation (MAD)1
Skewness0.58454651
Sum7875
Variance2.0553539
MonotonicityNot monotonic
2023-10-20T16:27:49.339412image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
3 945
40.9%
5 420
18.2%
2 315
 
13.6%
4 210
 
9.1%
7 105
 
4.5%
1 105
 
4.5%
6 105
 
4.5%
(Missing) 105
 
4.5%
ValueCountFrequency (%)
1 105
 
4.5%
2 315
 
13.6%
3 945
40.9%
4 210
 
9.1%
5 420
18.2%
6 105
 
4.5%
7 105
 
4.5%
ValueCountFrequency (%)
7 105
 
4.5%
6 105
 
4.5%
5 420
18.2%
4 210
 
9.1%
3 945
40.9%
2 315
 
13.6%
1 105
 
4.5%

post_VRF27
Categorical

HIGH CORRELATION  MISSING 

Distinct5
Distinct (%)0.2%
Missing105
Missing (%)4.5%
Memory size100.6 KiB
3.0
840 
2.0
525 
1.0
420 
4.0
210 
5.0
210 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters6615
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.0
2nd row3.0
3rd row3.0
4th row3.0
5th row3.0

Common Values

ValueCountFrequency (%)
3.0 840
36.4%
2.0 525
22.7%
1.0 420
18.2%
4.0 210
 
9.1%
5.0 210
 
9.1%
(Missing) 105
 
4.5%

Length

2023-10-20T16:27:49.494632image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-20T16:27:49.687251image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
3.0 840
38.1%
2.0 525
23.8%
1.0 420
19.0%
4.0 210
 
9.5%
5.0 210
 
9.5%

Most occurring characters

ValueCountFrequency (%)
. 2205
33.3%
0 2205
33.3%
3 840
 
12.7%
2 525
 
7.9%
1 420
 
6.3%
4 210
 
3.2%
5 210
 
3.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4410
66.7%
Other Punctuation 2205
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2205
50.0%
3 840
 
19.0%
2 525
 
11.9%
1 420
 
9.5%
4 210
 
4.8%
5 210
 
4.8%
Other Punctuation
ValueCountFrequency (%)
. 2205
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 6615
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 2205
33.3%
0 2205
33.3%
3 840
 
12.7%
2 525
 
7.9%
1 420
 
6.3%
4 210
 
3.2%
5 210
 
3.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6615
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 2205
33.3%
0 2205
33.3%
3 840
 
12.7%
2 525
 
7.9%
1 420
 
6.3%
4 210
 
3.2%
5 210
 
3.2%

EHQ1_F
Categorical

HIGH CORRELATION  MISSING 

Distinct8
Distinct (%)0.5%
Missing840
Missing (%)36.4%
Memory size85.2 KiB
7
420 
2
315 
nein
210 
105 
1,2
105 
Other values (3)
315 

Length

Max length5
Median length1
Mean length2
Min length1

Characters and Unicode

Total characters2940
Distinct characters10
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
7 420
18.2%
2 315
 
13.6%
nein 210
 
9.1%
105
 
4.5%
1,2 105
 
4.5%
2,4 105
 
4.5%
4 105
 
4.5%
keine 105
 
4.5%
(Missing) 840
36.4%

Length

2023-10-20T16:27:49.874345image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-20T16:27:50.089862image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
7 420
30.8%
2 315
23.1%
nein 210
15.4%
1,2 105
 
7.7%
2,4 105
 
7.7%
4 105
 
7.7%
keine 105
 
7.7%

Most occurring characters

ValueCountFrequency (%)
2 525
17.9%
n 525
17.9%
7 420
14.3%
e 420
14.3%
i 315
10.7%
, 210
 
7.1%
4 210
 
7.1%
105
 
3.6%
1 105
 
3.6%
k 105
 
3.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1365
46.4%
Decimal Number 1260
42.9%
Other Punctuation 210
 
7.1%
Space Separator 105
 
3.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 525
41.7%
7 420
33.3%
4 210
 
16.7%
1 105
 
8.3%
Lowercase Letter
ValueCountFrequency (%)
n 525
38.5%
e 420
30.8%
i 315
23.1%
k 105
 
7.7%
Other Punctuation
ValueCountFrequency (%)
, 210
100.0%
Space Separator
ValueCountFrequency (%)
105
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1575
53.6%
Latin 1365
46.4%

Most frequent character per script

Common
ValueCountFrequency (%)
2 525
33.3%
7 420
26.7%
, 210
 
13.3%
4 210
 
13.3%
105
 
6.7%
1 105
 
6.7%
Latin
ValueCountFrequency (%)
n 525
38.5%
e 420
30.8%
i 315
23.1%
k 105
 
7.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2940
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 525
17.9%
n 525
17.9%
7 420
14.3%
e 420
14.3%
i 315
10.7%
, 210
 
7.1%
4 210
 
7.1%
105
 
3.6%
1 105
 
3.6%
k 105
 
3.6%

mistake_flag
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size100.6 KiB
0
2145 
1
 
165

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2310
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 2145
92.9%
1 165
 
7.1%

Length

2023-10-20T16:27:50.270957image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-20T16:27:50.422777image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
0 2145
92.9%
1 165
 
7.1%

Most occurring characters

ValueCountFrequency (%)
0 2145
92.9%
1 165
 
7.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2310
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2145
92.9%
1 165
 
7.1%

Most occurring scripts

ValueCountFrequency (%)
Common 2310
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2145
92.9%
1 165
 
7.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2310
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2145
92.9%
1 165
 
7.1%

Interactions

2023-10-20T16:27:15.511418image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:25:09.264180image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:25:15.268266image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:25:19.143201image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:25:24.102567image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:25:29.536018image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:25:34.283284image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:25:39.419936image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:25:44.568664image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:25:49.291363image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:25:53.606963image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:25:58.327560image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:26:03.854797image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:26:09.167549image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:26:13.955112image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:26:19.028939image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:26:23.860213image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:26:28.237179image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:26:33.401433image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:26:38.112149image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:26:42.906702image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:26:47.936979image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:26:52.665085image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:26:57.121191image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:27:02.294966image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:27:06.697195image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:27:11.087752image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:27:15.677557image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:25:09.404654image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:25:15.392411image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:25:19.330862image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:25:24.312012image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:25:29.745715image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:25:34.483059image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:25:39.649817image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:25:44.743466image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:25:49.455285image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:25:53.782140image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:25:58.510407image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:26:04.025435image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:26:09.340839image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:26:14.170138image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:26:19.219654image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:26:24.039550image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:26:28.410179image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:26:33.602690image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:26:38.283764image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:26:43.080821image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:26:48.175953image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:26:52.842813image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:26:57.296031image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:27:02.468058image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:27:06.868540image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:27:11.276246image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:27:15.833108image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:25:09.525770image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:25:15.507024image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:25:19.504257image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:25:24.498484image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:25:29.940737image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:25:34.668896image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:25:39.820840image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:25:44.905421image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:25:49.612024image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:25:53.952130image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:25:58.680489image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:26:04.196886image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:26:09.532879image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:26:14.406630image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:26:19.396022image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:26:24.192757image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:26:28.571198image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:26:33.786567image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:26:38.447270image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:26:43.234988image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:26:48.357246image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:26:52.999517image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:26:57.457340image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:27:02.622056image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:27:07.024545image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:27:11.431850image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:27:16.008772image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:25:09.662334image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:25:15.632405image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:25:19.692360image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:25:24.684522image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:25:30.134450image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:25:34.863313image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:25:39.998937image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:25:45.085344image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:25:49.780885image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:25:54.126209image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:25:58.866939image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:26:04.476291image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:26:09.715885image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:26:14.594160image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:26:19.595943image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:26:24.357346image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:26:29.449952image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:26:33.973913image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:26:38.644306image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:26:43.405536image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:26:48.547839image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:26:53.166041image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:26:57.625458image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:27:02.835994image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:27:07.191597image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:27:11.602117image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:27:16.184362image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:25:09.814137image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:25:15.753618image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:25:19.872526image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:25:24.867950image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:25:30.305329image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:25:35.051065image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:25:40.184331image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:25:45.253295image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:25:49.938776image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:25:54.301684image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:25:59.036833image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:26:04.751578image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:26:09.950580image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:26:14.773382image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:26:19.772513image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:26:24.524184image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:26:29.562201image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:26:34.153927image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:26:38.816664image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:26:43.568780image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:26:48.772124image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:26:53.334730image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:26:57.798187image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:27:03.005818image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:27:07.384906image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:27:11.788729image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:27:16.362980image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:25:09.962164image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:25:15.870076image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:25:20.040861image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:25:25.040265image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:25:30.461668image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:25:35.232481image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:25:40.381874image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:25:45.414176image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:25:50.083582image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:25:54.466470image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:25:59.198841image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:26:05.013404image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:26:10.126220image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:26:14.949869image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:26:19.967077image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:26:24.672278image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:26:29.681741image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:26:34.311676image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:26:38.975050image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:26:43.728451image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:26:48.951386image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:26:53.489435image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:26:57.967592image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
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2023-10-20T16:26:41.665514image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:26:46.476998image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:26:51.507179image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:26:55.980417image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:27:01.145953image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:27:05.583381image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:27:09.950717image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:27:14.380395image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:27:19.067304image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:25:14.422822image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:25:18.059990image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:25:23.027847image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:25:28.439061image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:25:33.244311image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:25:38.349845image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:25:43.571098image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:25:48.191819image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:25:52.651430image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:25:57.300566image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:26:02.035206image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:26:08.042347image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:26:12.828778image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:26:17.944557image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:26:22.895501image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:26:27.276016image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:26:32.332908image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:26:37.025060image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:26:41.827932image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:26:46.712734image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:26:51.669338image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:26:56.149254image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:27:01.320869image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:27:05.743876image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:27:10.113634image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:27:14.541567image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:27:19.245121image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:25:14.561704image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:25:18.228733image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:25:23.224531image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:25:28.650227image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:25:33.419672image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:25:38.539509image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:25:43.731836image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:25:48.359631image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:25:52.813596image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:25:57.487842image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:26:02.272829image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:26:08.215035image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:26:12.992250image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:26:18.130549image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:26:23.065139image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:26:27.441841image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:26:32.501949image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:26:37.208574image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:26:42.021609image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:26:46.909336image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:26:51.836083image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:26:56.312297image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:27:01.488722image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:27:05.905700image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:27:10.290761image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:27:14.710149image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:27:19.422949image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:25:14.696121image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:25:18.400498image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:25:23.411425image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:25:28.843459image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:25:33.591341image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:25:38.710685image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:25:43.895891image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:25:48.550175image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:25:52.974367image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:25:57.653250image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:26:02.511912image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:26:08.389744image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:26:13.160166image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:26:18.308464image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:26:23.237031image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:26:27.597076image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:26:32.668100image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:26:37.387717image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:26:42.215755image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:26:47.117585image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:26:52.007189image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:26:56.472839image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:27:01.654230image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:27:06.069381image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:27:10.456369image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:27:14.873819image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:27:19.644166image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:25:14.861231image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:25:18.564168image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:25:23.587171image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:25:29.009651image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:25:33.761875image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:25:38.890997image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:25:44.061100image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:25:48.754580image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:25:53.125111image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:25:57.823589image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:26:03.416429image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:26:08.575567image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:26:13.333383image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:26:18.474029image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:26:23.395443image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:26:27.751495image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:26:32.828930image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:26:37.546760image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:26:42.405360image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:26:47.287350image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:26:52.180869image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:26:56.631764image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:27:01.812443image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:27:06.224430image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:27:10.611401image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:27:15.035452image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:27:19.805484image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:25:15.007710image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:25:18.728098image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:25:23.759263image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:25:29.186497image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:25:33.933327image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:25:39.069720image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:25:44.225318image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:25:48.949540image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:25:53.285075image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:25:57.990561image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:26:03.571929image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:26:08.760878image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:26:13.552990image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:26:18.638498image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:26:23.550023image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:26:27.909770image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:26:32.985094image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:26:37.727233image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:26:42.576812image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:26:47.482392image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:26:52.344358image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:26:56.795305image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:27:01.973098image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:27:06.379267image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:27:10.766097image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:27:15.194416image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:27:19.968207image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:25:15.142883image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:25:18.923644image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:25:23.927766image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:25:29.358029image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:25:34.106545image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:25:39.244318image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:25:44.388504image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:25:49.115183image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:25:53.446435image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:25:58.152696image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:26:03.699215image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:26:08.958604image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:26:13.769981image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:26:18.825830image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:26:23.702024image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:26:28.074072image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:26:33.173787image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:26:37.916861image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:26:42.739074image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:26:47.743028image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:26:52.497869image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:26:56.952755image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:27:02.132846image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:27:06.535970image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:27:10.920557image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-20T16:27:15.353097image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Correlations

2023-10-20T16:27:50.708984image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
levelCountertime_mscorrectCounterAGEprepostbody°post_VRF1post_VRF2post_VRF4post_VRF5post_VRF6post_VRF7post_VRF9post_VRF10post_VRF11post_VRF12post_VRF13post_VRF15post_VRF16post_VRF17post_VRF18post_VRF20post_VRF22post_VRF23post_VRF25post_VRF26ptcptrial_setfeedbackTypePARTICIPANT IDSEXpre_csq1pre_csq2pre_csq3pre_csq6pre_csq7pre_csq8pre_csq9pre_csq11pre_csq12pre_csq14pre_csq15post_csq1post_csq2post_csq3post_csq4post_csq5post_csq6post_csq7post_csq8post_csq9post_csq10post_csq11post_csq12post_csq13post_csq14post_csq15post_csq16EHQ1EHQ2EHQ3EHQ4EHQ5EHQ6EHQ7EHQ8EHQ9EHQ10EHQIEHQIIEHQ_Fpost_VRF3post_VRF8post_VRF14post_VRF19post_VRF21post_VRF24post_VRF27EHQ1_Fmistake_flag
levelCounter1.000-0.0480.9900.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.039
time_ms-0.0481.000-0.0760.0800.2290.079-0.1170.0400.011-0.109-0.0950.0090.1610.0730.002-0.0240.0690.099-0.0410.0970.125-0.059-0.0010.0640.101-0.1530.0430.0370.0110.0000.0370.0310.0000.0000.0000.0250.0000.0880.0000.0000.0000.0000.0000.0000.0000.0730.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0390.0000.0000.0160.0000.0000.0000.0230.0000.0000.0040.0450.0000.0230.0000.0140.0000.0000.0560.0430.0000.060
correctCounter0.990-0.0761.000-0.008-0.0070.0250.001-0.033-0.004-0.0030.038-0.007-0.0150.0110.0190.004-0.005-0.013-0.0180.023-0.0430.039-0.019-0.0270.0230.000-0.0130.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.000
AGE0.0000.080-0.0081.0000.353-0.1510.188-0.227-0.070-0.1100.131-0.1610.0720.114-0.033-0.038-0.0920.1000.1060.020-0.0910.280-0.1700.2910.3540.080-0.0040.9960.0000.0000.9960.2760.7940.4980.4700.3370.9990.4000.4760.5480.5480.7950.3720.3400.3200.3410.2221.0000.5150.5440.5460.3510.3510.4290.5460.3510.4430.4150.3510.4000.4780.3770.4280.4420.2930.4640.3950.3890.3770.4690.4110.8130.4820.3990.3920.4330.4850.4520.4810.8130.068
pre0.0000.229-0.0070.3531.0000.4110.058-0.202-0.188-0.0630.111-0.3400.2920.076-0.3440.1370.3660.2010.048-0.0910.0280.197-0.001-0.0730.250-0.3170.3130.9970.0000.0000.9970.5080.3930.6820.2830.3200.9990.6880.5870.3150.3150.2590.7140.3500.6030.3610.3811.0000.3610.5460.3110.3110.9990.4720.3110.3490.3270.7250.3490.3150.7760.8410.5470.5150.6290.5110.5760.8270.4580.5990.5500.6900.6440.6040.6060.5440.5290.6800.5240.6900.208
post0.0000.0790.025-0.1510.4111.000-0.151-0.0160.401-0.1180.068-0.042-0.253-0.191-0.0130.2370.3760.051-0.094-0.059-0.185-0.0500.443-0.2160.049-0.2310.3480.9990.0000.0000.9990.8860.9960.9960.9960.9960.9960.9960.8460.9960.9960.8460.9120.8920.9580.9560.8931.0000.9970.9960.9960.5390.9960.9560.9960.6830.9970.9410.6830.9960.9970.9330.8260.9680.9970.9330.9100.9660.9700.9200.9320.9630.8890.9710.9500.9680.9190.9300.8800.9630.233
body°0.000-0.1170.0010.1880.058-0.1511.000-0.065-0.1020.1580.1460.172-0.282-0.318-0.1180.1400.2440.3050.250-0.1400.0230.1510.0020.2920.1060.0970.4900.9970.0000.0000.9970.3300.9990.5650.8210.6840.9990.3530.2410.9990.9990.7090.6820.6230.7930.5380.6271.0000.8070.6820.9990.2530.6870.8690.9990.2530.9990.7470.2530.2340.3420.4660.4830.3910.4590.4660.4090.4650.3760.5270.4180.8050.5310.5490.5380.5300.5310.4500.5680.8050.000
post_VRF10.0000.040-0.033-0.227-0.202-0.016-0.0651.0000.4870.028-0.2870.102-0.255-0.566-0.1260.2570.233-0.0110.182-0.1630.389-0.2080.285-0.354-0.2880.469-0.0030.9970.0000.0000.9970.5360.4050.4050.4610.4640.3970.3500.8590.3500.3500.7330.4350.4610.4530.5570.3211.0000.4240.4290.3500.6870.3970.3840.3500.5450.3710.4320.5450.3500.4380.4770.5870.4780.4110.4770.4890.5850.6680.3340.5720.7600.5000.6120.5460.6310.4850.4510.5680.7600.128
post_VRF20.0000.011-0.004-0.070-0.1880.401-0.1020.4871.000-0.057-0.1070.239-0.499-0.487-0.0540.4600.3060.1490.200-0.4160.137-0.0970.417-0.121-0.3260.2410.1970.9970.0000.0000.9970.4570.2950.5000.6400.5100.5460.4590.8080.4590.4590.3030.5460.3060.4560.7540.4451.0000.5070.5350.4590.2540.5460.5180.4590.2540.5830.5440.2540.2540.3300.3380.3500.3900.4660.5180.4360.4440.5990.3980.5260.6750.3900.8470.5350.6120.4810.3730.4490.6750.045
post_VRF40.000-0.109-0.003-0.110-0.063-0.1180.1580.028-0.0571.000-0.0440.470-0.448-0.1560.4250.4120.2920.4820.700-0.064-0.065-0.4300.3520.229-0.0360.1660.4130.9970.0000.0000.9970.7200.5240.4830.4750.4910.3970.3970.7290.3970.3970.7450.5050.4610.3260.5430.2891.0000.4630.4750.3970.3970.6870.4300.3970.3970.4260.5350.3970.3970.5380.5980.5660.5430.4270.5900.5920.6440.5530.5680.5220.6670.5820.5260.6620.5240.6050.5560.6220.6670.125
post_VRF50.000-0.0950.0380.1310.1110.0680.146-0.287-0.107-0.0441.000-0.2940.1920.233-0.458-0.176-0.435-0.304-0.331-0.103-0.2010.579-0.2950.0850.6540.175-0.2110.9970.0000.0000.9970.3500.4580.5450.8390.7480.5450.5450.6680.4580.4580.7640.5480.4610.5300.5230.4331.0000.5160.5950.4580.5450.5450.4300.4580.5450.4700.5450.5450.5450.5420.4720.4250.4970.6950.5570.4990.5930.5620.4400.5320.7150.4640.5800.6070.4600.4910.6490.5950.7150.143
post_VRF60.0000.009-0.007-0.161-0.340-0.0420.1720.1020.2390.470-0.2941.000-0.634-0.2400.5100.3910.2790.7300.448-0.4210.044-0.5440.4690.189-0.400-0.1010.4860.9970.0000.0000.9970.3790.3650.5280.3300.3790.2300.2300.5090.2300.2300.4730.4560.3550.4250.3530.3411.0000.3260.4410.2300.6880.2300.3160.2300.5460.2360.3720.5460.2300.5880.5060.3970.4800.4850.4560.5750.5710.5250.2890.4220.6270.6040.4550.5760.5580.5080.5880.5540.6270.138
post_VRF70.0000.161-0.0150.0720.292-0.253-0.282-0.255-0.499-0.4480.192-0.6341.0000.606-0.404-0.619-0.422-0.441-0.5790.249-0.0040.451-0.528-0.0810.296-0.287-0.5440.9970.0000.0000.9970.3850.7620.6140.8950.7720.5450.3970.3680.9990.9990.7930.5850.7260.6980.4800.5801.0000.8180.5450.9990.6870.3970.6150.9990.3970.8570.6540.3970.3970.5590.6170.6220.7860.4360.7960.5730.5420.4440.4830.3910.6550.5130.5450.5170.4400.5760.5410.4840.6550.127
post_VRF90.0000.0730.0110.1140.076-0.191-0.318-0.566-0.487-0.1560.233-0.2400.6061.0000.162-0.432-0.566-0.214-0.5710.227-0.5270.154-0.3220.1830.376-0.231-0.3120.9970.0000.0000.9970.4870.7220.3900.6060.4870.3130.3130.5760.9990.9990.9990.8150.6330.7050.4510.5251.0000.7240.5400.9990.3130.9990.4800.9990.4590.7770.8810.4590.3130.4060.4760.6040.3550.4390.4910.4310.4520.4080.5130.4300.6710.4740.5550.4930.4650.3790.4620.5520.6710.044
post_VRF100.0000.0020.019-0.033-0.344-0.013-0.118-0.126-0.0540.425-0.4580.510-0.4040.1621.0000.203-0.0020.3450.3090.313-0.343-0.3870.2820.172-0.205-0.0700.1460.9970.0000.0000.9970.3470.7150.6000.6400.5140.5460.2810.3900.2810.2810.4110.3650.5120.4470.4490.4691.0000.4310.4610.2810.3970.3970.5890.2810.2810.5240.3770.2810.2810.5530.5350.4610.3330.4400.3940.4930.6490.5610.2490.4160.8350.5500.4580.5910.5110.5400.5830.6060.8350.160
post_VRF110.000-0.0240.004-0.0380.1370.2370.1400.2570.4600.412-0.1760.391-0.619-0.4320.2031.0000.5590.4360.600-0.3270.211-0.1420.2670.036-0.3640.2120.5450.9970.0000.0000.9970.4170.7920.4670.5400.4860.3970.4590.3410.4590.4590.4640.4900.6890.5510.3760.6691.0000.4950.3280.4590.3970.5460.4540.4590.5460.4710.5070.5460.5460.6250.4860.4690.5700.4760.5060.3650.5300.4910.5190.5440.6950.5360.4910.5070.4010.5400.6840.4660.6950.052
post_VRF120.0000.069-0.005-0.0920.3660.3760.2440.2330.3060.292-0.4350.279-0.422-0.566-0.0020.5591.0000.5700.478-0.1520.302-0.3180.378-0.205-0.393-0.1990.7250.9970.0000.0000.9970.5500.9990.6870.8580.6160.3500.4580.4690.5450.5450.5940.4830.5280.7430.5970.4691.0000.4840.4750.5450.5450.5450.5230.5450.3500.5600.5160.3500.3500.4190.4850.5960.5250.4570.6100.5860.6050.4820.3980.5350.6080.4780.5050.5900.5110.5960.5480.6170.6080.109
post_VRF130.0000.099-0.0130.1000.2010.0510.305-0.0110.1490.482-0.3040.730-0.441-0.2140.3450.4360.5701.0000.586-0.4080.056-0.3590.4290.039-0.294-0.3910.7030.9970.0000.0000.9970.7310.5240.5510.5150.6290.4580.3970.8960.3970.3970.5280.4080.4470.5240.4630.2651.0000.5530.4910.3970.4580.3500.4680.3970.3500.4260.4140.3500.3970.3360.4410.5160.4310.3930.4150.4360.4740.6030.6330.6000.6710.5620.5440.7270.6580.6510.5200.6210.6710.078
post_VRF150.000-0.041-0.0180.1060.048-0.0940.2500.1820.2000.700-0.3310.448-0.579-0.5710.3090.6000.4780.5861.000-0.1560.281-0.3670.2540.121-0.4050.1950.4460.9970.0000.0000.9970.4970.7620.5800.4960.4750.3120.3120.6100.4580.4580.7640.4290.6400.5540.8280.5631.0000.4380.7080.4580.3120.4580.6940.4580.4580.5830.4530.4580.6870.4740.5130.5530.4750.5320.5370.4900.4610.4670.5050.5450.7410.4850.5050.5520.5260.4950.4970.5410.7410.094
post_VRF160.0000.0970.0230.020-0.091-0.059-0.140-0.163-0.416-0.064-0.103-0.4210.2490.2270.313-0.327-0.152-0.408-0.1561.000-0.1020.038-0.2790.0730.2920.075-0.3360.9970.0000.0000.9970.3120.4050.5600.4620.4170.6880.5460.3500.3500.3500.4110.6420.4620.5440.3990.3941.0000.4770.4170.3500.6880.5460.4900.3500.3500.4570.5980.3500.5460.5210.5380.2120.3860.7020.5140.3960.4840.5330.7430.5030.8850.4790.7030.5260.5040.5310.5090.5000.8850.119
post_VRF170.0000.125-0.043-0.0910.028-0.1850.0230.3890.137-0.065-0.2010.044-0.004-0.527-0.3430.2110.3020.0560.281-0.1021.0000.023-0.2340.180-0.3880.0850.0260.9970.0000.0000.9970.3980.7620.7910.6400.5360.6870.4580.5630.9990.9990.8490.6390.6400.5800.4210.4891.0000.7740.6160.9990.6870.3970.5760.9990.3970.7590.6870.3970.4580.4260.5580.5630.4800.4600.5230.5250.3810.5520.6440.4690.7360.6550.5570.6250.5740.5650.4130.6260.7360.122
post_VRF180.000-0.0590.0390.2800.197-0.0500.151-0.208-0.097-0.4300.579-0.5440.4510.154-0.387-0.142-0.318-0.359-0.3670.0380.0231.000-0.616-0.0960.339-0.019-0.4230.9970.0000.0000.9970.5950.5910.5000.4250.5950.5450.5450.4330.5450.5450.5100.4480.6650.4620.4300.4331.0000.4840.6160.5450.5450.5450.4620.5450.4580.5440.4940.4580.5450.5250.5140.5080.5710.6410.5340.4960.4640.4670.4600.5320.6940.4070.5360.5980.5840.5510.5800.5250.6940.176
post_VRF200.000-0.001-0.019-0.170-0.0010.4430.0020.2850.4170.352-0.2950.469-0.528-0.3220.2820.2670.3780.4290.254-0.279-0.234-0.6161.000-0.103-0.143-0.1000.4690.9970.0000.0000.9970.3620.5070.7940.3790.4870.3970.4590.4890.3130.3130.2860.3840.5280.4350.5270.4981.0000.4250.4700.3130.6880.3970.3690.3130.6880.3210.3930.6880.3130.4230.5390.4940.4640.5330.6270.4840.3760.5350.5720.4890.6180.3050.4340.5080.4580.4970.5500.5270.6180.111
post_VRF220.0000.064-0.0270.291-0.073-0.2160.292-0.354-0.1210.2290.0850.189-0.0810.1830.1720.036-0.2050.0390.1210.0730.180-0.096-0.1031.0000.2050.1150.1360.9970.0000.0000.9970.3650.4060.5280.6500.5460.3500.3970.3210.5460.5460.7930.6400.3310.5910.3720.6131.0000.5760.4050.5460.3970.5460.7170.5460.3970.8050.5220.3970.3970.3770.4770.3970.4380.3590.3970.4120.4620.4560.3240.4340.6240.4950.5360.4180.4570.5940.5060.6130.6240.091
post_VRF230.0000.1010.0230.3540.2500.0490.106-0.288-0.326-0.0360.654-0.4000.2960.376-0.205-0.364-0.393-0.294-0.4050.292-0.3880.339-0.1430.2051.000-0.026-0.1900.9970.0000.0000.9970.4170.3650.4130.3850.3960.2540.6880.3660.2540.2540.3720.3950.5070.5480.4670.5911.0000.3650.3960.2540.6880.5460.5030.2540.6880.4230.4210.6880.6880.6070.4570.3600.4360.5550.3990.4280.4250.4160.5210.5770.7230.4560.4470.4750.3820.4670.4830.4580.7230.068
post_VRF250.000-0.1530.0000.080-0.317-0.2310.0970.4690.2410.1660.175-0.101-0.287-0.231-0.0700.212-0.199-0.3910.1950.0750.085-0.019-0.1000.115-0.0261.000-0.2410.9970.0000.0000.9970.5120.4280.5930.3580.4590.3500.3500.6020.3970.3970.3080.4000.5150.5530.4020.5461.0000.4110.3720.3970.4590.3500.4060.3970.3970.4270.4090.3970.4590.7470.8120.2380.4540.6890.5500.5460.7810.6830.4070.4650.6120.4470.4190.6560.6000.5970.5880.4270.6120.055
post_VRF260.0000.043-0.013-0.0040.3130.3480.490-0.0030.1970.413-0.2110.486-0.544-0.3120.1460.5450.7250.7030.446-0.3360.026-0.4230.4690.136-0.190-0.2411.0000.9970.0000.0000.9970.5580.7620.5360.5950.5340.2530.4580.3650.4580.4580.3030.4870.3060.7450.5030.6271.0000.5040.5340.4580.4580.5450.7480.4580.2530.7770.5050.2530.2530.3860.4660.4330.3680.4850.4540.4990.5120.4350.4470.4720.7260.5180.6460.6080.6690.5250.5090.5490.7260.122
ptcp0.0000.0370.0000.9960.9970.9990.9970.9970.9970.9970.9970.9970.9970.9970.9970.9970.9970.9970.9970.9970.9970.9970.9970.9970.9970.9970.9971.0000.0000.0001.0000.9960.9960.9960.9960.9960.9960.9960.9960.9960.9960.9960.9960.9960.9960.9960.9961.0000.9960.9960.9960.9960.9960.9960.9960.9960.9960.9960.9960.9960.9960.9960.9960.9960.9960.9960.9960.9960.9960.9960.9960.9980.9960.9960.9960.9960.9960.9960.9960.9980.230
trial_set0.0000.0110.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.026
feedbackType0.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
PARTICIPANT ID0.0000.0370.0000.9960.9970.9990.9970.9970.9970.9970.9970.9970.9970.9970.9970.9970.9970.9970.9970.9970.9970.9970.9970.9970.9970.9970.9971.0000.0000.0001.0000.9960.9960.9960.9960.9960.9960.9960.9960.9960.9960.9960.9960.9960.9960.9960.9961.0000.9960.9960.9960.9960.9960.9960.9960.9960.9960.9960.9960.9960.9960.9960.9960.9960.9960.9960.9960.9960.9960.9960.9960.9980.9960.9960.9960.9960.9960.9960.9960.9980.230
SEX0.0000.0310.0000.2760.5080.8860.3300.5360.4570.7200.3500.3790.3850.4870.3470.4170.5500.7310.4970.3120.3980.5950.3620.3650.4170.5120.5580.9960.0000.0000.9961.0000.1780.1050.2260.2540.1140.1140.0970.1140.1140.1680.3320.2070.1570.2700.1951.0000.2700.2330.1040.1040.4580.2700.1040.1040.1550.5840.1040.1140.2140.1790.0850.1190.2460.1790.4200.1940.6000.0920.3850.7470.1350.4860.5590.5200.4180.2680.3550.7470.000
pre_csq10.0000.0000.0000.7940.3930.9960.9990.4050.2950.5240.4580.3650.7620.7220.7150.7920.9990.5240.7620.4050.7620.5910.5070.4060.3650.4280.7620.9960.0000.0000.9960.1781.0000.3380.4410.3280.0660.0660.1290.6850.6850.4440.4550.3240.7070.1900.1061.0000.6920.2470.6840.0690.0690.2470.6840.0690.6840.6950.0690.0660.1290.0660.2020.2790.1690.0660.4110.2270.3060.1110.2530.8390.6660.2450.2600.2450.4060.2760.2600.8390.015
pre_csq20.0000.0000.0000.4980.6820.9960.5650.4050.5000.4830.5450.5280.6140.3900.6000.4670.6870.5510.5800.5600.7910.5000.7940.5280.4130.5930.5360.9960.0000.0000.9960.1050.3381.0000.4260.4700.2150.2150.1300.2150.2150.0000.1060.1800.3420.3230.1531.0000.5860.2640.2310.2100.2100.2380.2310.2100.3390.3390.2100.2150.2170.4700.0000.3880.4990.5580.5690.2260.4970.2910.4190.6690.3180.3500.3010.3010.4020.4140.5180.6690.067
pre_csq30.0000.0000.0000.4700.2830.9960.8210.4610.6400.4750.8390.3300.8950.6060.6400.5400.8580.5150.4960.4620.6400.4250.3790.6500.3850.3580.5950.9960.0000.0000.9960.2260.4410.4261.0000.8400.0860.0860.1630.5440.5440.3280.3640.3190.8070.2270.2131.0000.7790.1370.5430.0910.0910.6080.5430.0910.7930.5680.0910.0860.1640.1310.2560.5480.2950.6020.5780.0870.3510.0360.3310.6760.2340.5760.3560.2730.3850.3410.5840.6760.000
pre_csq60.0000.0250.0000.3370.3200.9960.6840.4640.5100.4910.7480.3790.7720.4870.5140.4860.6160.6290.4750.4170.5360.5950.4870.5460.3960.4590.5340.9960.0000.0000.9960.2540.3280.4700.8401.0000.0980.0980.1850.4600.4600.2580.2690.6060.6680.3870.4931.0000.8760.3800.4580.1040.1040.4770.4580.1040.6680.4710.1040.4600.2730.2400.0090.4940.3580.5770.5690.1970.3270.0650.4610.5610.2380.4460.4180.2550.3120.3120.3650.5610.019
pre_csq70.0000.0000.0000.9990.9990.9960.9990.3970.5460.3970.5450.2300.5450.3130.5460.3970.3500.4580.3120.6880.6870.5450.3970.3500.2540.3500.2530.9960.0000.0000.9960.1140.0660.2150.0860.0981.0000.0370.0810.0370.0370.0620.1460.0910.2330.1210.0861.0000.1210.4580.0400.0400.0400.4600.0400.0400.0660.1540.0400.0370.0820.2370.1300.1970.1790.2370.2860.3180.1950.3170.3170.9980.2830.2550.2550.5460.3980.1890.4590.9980.000
pre_csq80.0000.0880.0000.4000.6880.9960.3530.3500.4590.3970.5450.2300.3970.3130.2810.4590.4580.3970.3120.5460.4580.5450.4590.3970.6880.3500.4580.9960.0000.0000.9960.1140.0660.2150.0860.0980.0371.0000.0810.0370.0370.0620.4010.0910.2330.1210.0861.0000.4600.1040.0400.0400.0400.1210.0400.0400.0660.3980.0400.0370.0820.2370.1300.2610.3180.2160.2860.1620.1950.1450.9991.0000.2830.2550.2550.3980.5460.1890.2821.0000.061
pre_csq90.0000.0000.0000.4760.5870.8460.2410.8590.8080.7290.6680.5090.3680.5760.3900.3410.4690.8960.6100.3500.5630.4330.4890.3210.3660.6020.3650.9960.0000.0000.9960.0970.1290.1300.1630.1850.0810.0811.0000.0810.0810.3320.2700.1730.2100.1640.1631.0000.2260.1950.0860.0860.0860.2260.0860.0860.1290.2860.0860.0810.1550.1750.3500.1130.1230.1750.4620.0820.5960.0000.4360.4330.2740.6270.6540.6270.3220.2420.4480.4330.021
pre_csq110.0000.0000.0000.5480.3150.9960.9990.3500.4590.3970.4580.2300.9990.9990.2810.4590.5450.3970.4580.3500.9990.5450.3130.5460.2540.3970.4580.9960.0000.0000.9960.1140.6850.2150.5440.4600.0370.0370.0811.0000.9950.6860.6900.5430.6890.1210.0861.0001.0000.4580.9950.0400.0400.4600.9950.0401.0001.0000.0400.0370.0820.2370.1300.1970.3180.2160.2860.1620.1950.3170.3170.6760.4600.3980.2550.3980.3510.1890.3980.6760.000
pre_csq120.0000.0000.0000.5480.3150.9960.9990.3500.4590.3970.4580.2300.9990.9990.2810.4590.5450.3970.4580.3500.9990.5450.3130.5460.2540.3970.4580.9960.0000.0000.9960.1140.6850.2150.5440.4600.0370.0370.0810.9951.0000.6860.6900.5430.6890.1210.0861.0001.0000.4580.9950.0400.0400.4600.9950.0401.0001.0000.0400.0370.0820.2370.1300.1970.3180.2160.2860.1620.1950.3170.3170.6760.4600.3980.2550.3980.3510.1890.3980.6760.000
pre_csq140.0000.0000.0000.7950.2590.8460.7090.7330.3030.7450.7640.4730.7930.9990.4110.4640.5940.5280.7640.4110.8490.5100.2860.7930.3720.3080.3030.9960.0000.0000.9960.1680.4440.0000.3280.2580.0620.0620.3320.6860.6861.0000.4590.3240.4810.2580.1291.0000.6940.2530.6850.0660.0660.2580.6850.0660.6900.6970.0660.0620.1220.3450.1580.2870.1410.0640.2780.2370.2860.1200.4000.6760.4120.2550.2550.2550.5110.2780.5290.6760.000
pre_csq150.0000.0000.0000.3720.7140.9120.6820.4350.5460.5050.5480.4560.5850.8150.3650.4900.4830.4080.4290.6420.6390.4480.3840.6400.3950.4000.4870.9960.0000.0000.9960.3320.4550.1060.3640.2690.1460.4010.2700.6900.6900.4591.0000.3800.4250.2780.4141.0000.5280.3420.6890.1550.6890.3400.6890.3990.5611.0000.3990.1460.1900.2610.2280.1790.2820.2410.4030.2750.3610.1680.4120.6480.4950.5670.5400.4480.3700.3060.4500.6480.066
post_csq10.0000.0000.0000.3400.3500.8920.6230.4610.3060.4610.4610.3550.7260.6330.5120.6890.5280.4470.6400.4620.6400.6650.5280.3310.5070.5150.3060.9960.0000.0000.9960.2070.3240.1800.3190.6060.0910.0910.1730.5430.5430.3240.3801.0000.6240.1590.7911.0000.6080.4880.5430.0910.0910.4880.5430.5430.5430.5830.5430.5430.3330.1900.0190.2820.3420.4640.4380.3380.6200.0000.4380.4060.2340.2730.5130.2730.3100.6170.2170.4060.000
post_csq20.0000.0000.0000.3200.6030.9580.7930.4530.4560.3260.5300.4250.6980.7050.4470.5510.7430.5240.5540.5440.5800.4620.4350.5910.5480.5530.7450.9960.0000.0000.9960.1570.7070.3420.8070.6680.2330.2330.2100.6890.6890.4810.4250.6241.0000.2010.4471.0000.5460.2940.6890.2330.2330.5460.6890.2560.7070.5750.2560.2560.1980.3420.5200.1850.4400.4050.4810.2820.4530.4070.4620.7580.3660.4010.4350.3600.3840.3280.4670.7580.031
post_csq30.0000.0730.0000.3410.3610.9560.5380.5570.7540.5430.5230.3530.4800.4510.4490.3760.5970.4630.8280.3990.4210.4300.5270.3720.4670.4020.5030.9960.0000.0000.9960.2700.1900.3230.2270.3870.1210.1210.1640.1210.1210.2580.2780.1590.2011.0000.1641.0000.2820.1210.1210.1210.1210.1210.1210.1210.1250.2770.1210.4600.1980.1830.2560.3540.3130.3780.5030.1690.3180.3190.4240.5230.2700.4550.4390.3610.3320.3900.3980.5230.074
post_csq40.0000.0000.0000.2220.3810.8930.6270.3210.4450.2890.4330.3410.5800.5250.4690.6690.4690.2650.5630.3940.4890.4330.4980.6130.5910.5460.6270.9960.0000.0000.9960.1950.1060.1530.2130.4930.0860.0860.1630.0860.0860.1290.4140.7910.4470.1641.0001.0000.4960.1450.0860.0860.0860.5860.0860.5440.5510.4130.5440.5440.3370.4270.0360.5200.3390.4270.6440.5770.6210.2870.3830.3980.3230.4460.4700.3660.3690.6190.4150.3980.000
post_csq51.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
post_csq60.0000.0000.0000.5150.3610.9970.8070.4240.5070.4630.5160.3260.8180.7240.4310.4950.4840.5530.4380.4770.7740.4840.4250.5760.3650.4110.5040.9960.0000.0000.9960.2700.6920.5860.7790.8760.1210.4600.2261.0001.0000.6940.5280.6080.5460.2820.4961.0001.0000.4771.0000.1210.1210.4691.0000.1210.7780.7370.1210.4600.1980.2420.1480.4680.3420.4600.4580.1750.2950.4420.4840.6000.4270.3740.3610.3370.4800.2670.4500.6000.000
post_csq70.0000.0000.0000.5440.5460.9960.6820.4290.5350.4750.5950.4410.5450.5400.4610.3280.4750.4910.7080.4170.6160.6160.4700.4050.3960.3720.5340.9960.0000.0000.9960.2330.2470.2640.1370.3800.4580.1040.1950.4580.4580.2530.3420.4880.2940.1210.1451.0000.4771.0000.4580.1040.1040.3870.4580.1040.4680.5200.1040.4580.2700.1040.0300.1450.3470.2700.3810.1880.3440.1690.3400.7580.2380.3410.3650.5200.3650.3120.4150.7580.024
post_csq80.0000.0000.0000.5460.3110.9960.9990.3500.4590.3970.4580.2300.9990.9990.2810.4590.5450.3970.4580.3500.9990.5450.3130.5460.2540.3970.4580.9960.0000.0000.9960.1040.6840.2310.5430.4580.0400.0400.0860.9950.9950.6850.6890.5430.6890.1210.0861.0001.0000.4581.0000.0400.0400.4600.9950.0401.0001.0000.0400.0400.0860.2330.1370.1910.3150.2330.3130.1730.2090.3130.3510.6740.4600.3980.2550.3980.3510.1890.3980.6740.000
post_csq90.0000.0000.0000.3510.3110.5390.2530.6870.2540.3970.5450.6880.6870.3130.3970.3970.5450.4580.3120.6880.6870.5450.6880.3970.6880.4590.4580.9960.0000.0000.9960.1040.0690.2100.0910.1040.0400.0400.0860.0400.0400.0660.1550.0910.2330.1210.0861.0000.1210.1040.0401.0000.0400.1210.0400.0400.0660.1540.0400.0400.0860.2330.1370.1910.1910.2330.3980.1730.3980.3130.4591.0000.4600.2550.3980.3980.4590.1890.3981.0000.046
post_csq100.0000.0000.0000.3510.9990.9960.6870.3970.5460.6870.5450.2300.3970.9990.3970.5460.5450.3500.4580.5460.3970.5450.3970.5460.5460.3500.5450.9960.0000.0000.9960.4580.0690.2100.0910.1040.0400.0400.0860.0400.0400.0660.6890.0910.2330.1210.0861.0000.1210.1040.0400.0401.0000.1210.0400.0400.0661.0000.0400.0400.0860.2330.1370.2840.1910.2330.3130.1730.2090.1540.3130.4280.4600.6880.5460.2550.3510.4590.3980.4280.009
post_csq110.0000.0000.0000.4290.4720.9560.8690.3840.5180.4300.4300.3160.6150.4800.5890.4540.5230.4680.6940.4900.5760.4620.3690.7170.5030.4060.7480.9960.0000.0000.9960.2700.2470.2380.6080.4770.4600.1210.2260.4600.4600.2580.3400.4880.5460.1210.5861.0000.4690.3870.4600.1210.1211.0000.4600.4600.7780.4400.4600.1210.1590.2420.3520.2320.2590.1830.3670.2720.2040.4420.3090.6740.2700.5430.3610.5760.4250.3960.6360.6740.102
post_csq120.0000.0000.0000.5460.3110.9960.9990.3500.4590.3970.4580.2300.9990.9990.2810.4590.5450.3970.4580.3500.9990.5450.3130.5460.2540.3970.4580.9960.0000.0000.9960.1040.6840.2310.5430.4580.0400.0400.0860.9950.9950.6850.6890.5430.6890.1210.0861.0001.0000.4580.9950.0400.0400.4601.0000.0401.0001.0000.0400.0400.0860.2330.1370.1910.3150.2330.3130.1730.2090.3130.3510.6740.4600.3980.2550.3980.3510.1890.3980.6740.000
post_csq130.0000.0000.0000.3510.3490.6830.2530.5450.2540.3970.5450.5460.3970.4590.2810.5460.3500.3500.4580.3500.3970.4580.6880.3970.6880.3970.2530.9960.0000.0000.9960.1040.0690.2100.0910.1040.0400.0400.0860.0400.0400.0660.3990.5430.2560.1210.5441.0000.1210.1040.0400.0400.0400.4600.0401.0000.0660.3980.9950.0400.0860.2330.1370.2840.1910.2330.3510.3150.5460.1540.3130.4280.3980.2550.2550.5460.3980.5460.4590.4280.000
post_csq140.0000.0000.0000.4430.3270.9970.9990.3710.5830.4260.4700.2360.8570.7770.5240.4710.5600.4260.5830.4570.7590.5440.3210.8050.4230.4270.7770.9960.0000.0000.9960.1550.6840.3390.7930.6680.0660.0660.1291.0001.0000.6900.5610.5430.7070.1250.5511.0000.7780.4681.0000.0660.0660.7781.0000.0661.0000.7600.0660.0660.0890.2280.2030.2370.3230.2390.3290.2490.2150.3460.3600.6740.3790.5610.2620.4760.4060.1940.5610.6740.000
post_csq150.0000.0000.0000.4150.7250.9410.7470.4320.5440.5350.5450.3720.6540.8810.3770.5070.5160.4140.4530.5980.6870.4940.3930.5220.4210.4090.5050.9960.0000.0000.9960.5840.6950.3390.5680.4710.1540.3980.2861.0001.0000.6971.0000.5830.5750.2770.4131.0000.7370.5201.0000.1541.0000.4401.0000.3980.7601.0000.3980.1540.2010.2610.2490.3110.3640.3500.3540.2740.3000.3720.4070.6320.4040.5750.5180.4300.3010.3430.3660.6320.069
post_csq160.0000.0000.0000.3510.3490.6830.2530.5450.2540.3970.5450.5460.3970.4590.2810.5460.3500.3500.4580.3500.3970.4580.6880.3970.6880.3970.2530.9960.0000.0000.9960.1040.0690.2100.0910.1040.0400.0400.0860.0400.0400.0660.3990.5430.2560.1210.5441.0000.1210.1040.0400.0400.0400.4600.0400.9950.0660.3981.0000.0400.0860.2330.1370.2840.1910.2330.3510.3150.5460.1540.3130.4280.3980.2550.2550.5460.3980.5460.4590.4280.000
EHQ10.0000.0390.0000.4000.3150.9960.2340.3500.2540.3970.5450.2300.3970.3130.2810.5460.3500.3970.6870.5460.4580.5450.3130.3970.6880.4590.2530.9960.0000.0000.9960.1140.0660.2150.0860.4600.0370.0370.0810.0370.0370.0620.1460.5430.2560.4600.5441.0000.4600.4580.0400.0400.0400.1210.0400.0400.0660.1540.0401.0000.6900.2160.3530.2610.6900.2160.3540.3180.5480.1450.3170.5270.2830.2550.2550.2550.5460.5460.2820.5270.000
EHQ20.0000.0000.0000.4780.7760.9970.3420.4380.3300.5380.5420.5880.5590.4060.5530.6250.4190.3360.4740.5210.4260.5250.4230.3770.6070.7470.3860.9960.0000.0000.9960.2140.1290.2170.1640.2730.0820.0820.1550.0820.0820.1220.1900.3330.1980.1980.3371.0000.1980.2700.0860.0860.0860.1590.0860.0860.0890.2010.0860.6901.0000.7400.5250.2950.6230.2710.3110.7780.4770.2700.2850.6820.3540.2570.4630.3310.4240.9020.3660.6820.051
EHQ30.0000.0000.0000.3770.8410.9330.4660.4770.3380.5980.4720.5060.6170.4760.5350.4860.4850.4410.5130.5380.5580.5140.5390.4770.4570.8120.4660.9960.0000.0000.9960.1790.0660.4700.1310.2400.2370.2370.1750.2370.2370.3450.2610.1900.3420.1830.4271.0000.2420.1040.2330.2330.2330.2420.2330.2330.2280.2610.2330.2160.7401.0000.4100.3960.5450.5220.5260.7820.5290.7480.3860.7000.3340.3260.5380.3600.3630.7790.3350.7000.073
EHQ40.0000.0160.0000.4280.5470.8260.4830.5870.3500.5660.4250.3970.6220.6040.4610.4690.5960.5160.5530.2120.5630.5080.4940.3970.3600.2380.4330.9960.0000.0000.9960.0850.2020.0000.2560.0090.1300.1300.3500.1300.1300.1580.2280.0190.5200.2560.0361.0000.1480.0300.1370.1370.1370.3520.1370.1370.2030.2490.1370.3530.5250.4101.0000.1590.3220.4100.2720.4620.3830.4170.1370.6910.5470.2880.3100.3710.3100.4700.6120.6910.000
EHQ50.0000.0000.0000.4420.5150.9680.3910.4780.3900.5430.4970.4800.7860.3550.3330.5700.5250.4310.4750.3860.4800.5710.4640.4380.4360.4540.3680.9960.0000.0000.9960.1190.2790.3880.5480.4940.1970.2610.1130.1970.1970.2870.1790.2820.1850.3540.5201.0000.4680.1450.1910.1910.2840.2320.1910.2840.2370.3110.2840.2610.2950.3960.1591.0000.3260.8370.8510.3220.4500.6230.4140.7690.3100.4100.4600.3160.4750.4170.2810.7690.034
EHQ60.0000.0000.0000.2930.6290.9970.4590.4110.4660.4270.6950.4850.4360.4390.4400.4760.4570.3930.5320.7020.4600.6410.5330.3590.5550.6890.4850.9960.0000.0000.9960.2460.1690.4990.2950.3580.1790.3180.1230.3180.3180.1410.2820.3420.4400.3130.3391.0000.3420.3470.3150.1910.1910.2590.3150.1910.3230.3640.1910.6900.6230.5450.3220.3261.0000.4970.5530.5910.6900.3780.4630.7410.2480.4420.4490.3450.4080.6460.3660.7410.029
EHQ70.0000.0000.0000.4640.5110.9330.4660.4770.5180.5900.5570.4560.7960.4910.3940.5060.6100.4150.5370.5140.5230.5340.6270.3970.3990.5500.4540.9960.0000.0000.9960.1790.0660.5580.6020.5770.2370.2160.1750.2160.2160.0640.2410.4640.4050.3780.4271.0000.4600.2700.2330.2330.2330.1830.2330.2330.2390.3500.2330.2160.2710.5220.4100.8370.4971.0000.8150.3680.3660.5530.3670.6320.2810.4260.5680.2990.4320.4110.2170.6320.034
EHQ80.0000.0230.0000.3950.5760.9100.4090.4890.4360.5920.4990.5750.5730.4310.4930.3650.5860.4360.4900.3960.5250.4960.4840.4120.4280.5460.4990.9960.0000.0000.9960.4200.4110.5690.5780.5690.2860.2860.4620.2860.2860.2780.4030.4380.4810.5030.6441.0000.4580.3810.3130.3980.3130.3670.3130.3510.3290.3540.3510.3540.3110.5260.2720.8510.5530.8151.0000.5060.5740.6580.4960.6730.4980.3650.5180.3930.4400.4650.4430.6730.149
EHQ90.0000.0000.0000.3890.8270.9660.4650.5850.4440.6440.5930.5710.5420.4520.6490.5300.6050.4740.4610.4840.3810.4640.3760.4620.4250.7810.5120.9960.0000.0000.9960.1940.2270.2260.0870.1970.3180.1620.0820.1620.1620.2370.2750.3380.2820.1690.5771.0000.1750.1880.1730.1730.1730.2720.1730.3150.2490.2740.3150.3180.7780.7820.4620.3220.5910.3680.5061.0000.5760.3180.3490.6360.3120.4050.6100.4230.5420.8400.3120.6360.043
EHQ100.0000.0000.0000.3770.4580.9700.3760.6680.5990.5530.5620.5250.4440.4080.5610.4910.4820.6030.4670.5330.5520.4670.5350.4560.4160.6830.4350.9960.0000.0000.9960.6000.3060.4970.3510.3270.1950.1950.5960.1950.1950.2860.3610.6200.4530.3180.6211.0000.2950.3440.2090.3980.2090.2040.2090.5460.2150.3000.5460.5480.4770.5290.3830.4500.6900.3660.5740.5761.0000.6940.4620.7350.4560.5150.5880.6610.4820.4860.3640.7350.073
EHQI0.0000.0040.0000.4690.5990.9200.5270.3340.3980.5680.4400.2890.4830.5130.2490.5190.3980.6330.5050.7430.6440.4600.5720.3240.5210.4070.4470.9960.0000.0000.9960.0920.1110.2910.0360.0650.3170.1450.0000.3170.3170.1200.1680.0000.4070.3190.2871.0000.4420.1690.3130.3130.1540.4420.3130.1540.3460.3720.1540.1450.2700.7480.4170.6230.3780.5530.6580.3180.6941.0000.4720.9980.2780.4120.5050.5150.2030.5960.4820.9980.025
EHQII0.0000.0450.0000.4110.5500.9320.4180.5720.5260.5220.5320.4220.3910.4300.4160.5440.5350.6000.5450.5030.4690.5320.4890.4340.5770.4650.4720.9960.0000.0000.9960.3850.2530.4190.3310.4610.3170.9990.4360.3170.3170.4000.4120.4380.4620.4240.3831.0000.4840.3400.3510.4590.3130.3090.3510.3130.3600.4070.3130.3170.2850.3860.1370.4140.4630.3670.4960.3490.4620.4721.0000.7220.3630.3840.3980.4430.5330.4220.3580.7220.131
EHQ_F0.0000.0000.0000.8130.6900.9630.8050.7600.6750.6670.7150.6270.6550.6710.8350.6950.6080.6710.7410.8850.7360.6940.6180.6240.7230.6120.7260.9980.0000.0000.9980.7470.8390.6690.6760.5610.9981.0000.4330.6760.6760.6760.6480.4060.7580.5230.3981.0000.6000.7580.6741.0000.4280.6740.6740.4280.6740.6320.4280.5270.6820.7000.6910.7690.7410.6320.6730.6360.7350.9980.7221.0000.7450.7180.5340.7650.6860.6540.6791.0000.090
post_VRF30.0000.0230.0000.4820.6440.8890.5310.5000.3900.5820.4640.6040.5130.4740.5500.5360.4780.5620.4850.4790.6550.4070.3050.4950.4560.4470.5180.9960.0000.0000.9960.1350.6660.3180.2340.2380.2830.2830.2740.4600.4600.4120.4950.2340.3660.2700.3231.0000.4270.2380.4600.4600.4600.2700.4600.3980.3790.4040.3980.2830.3540.3340.5470.3100.2480.2810.4980.3120.4560.2780.3630.7451.0000.3620.4020.3110.4480.3800.4810.7450.093
post_VRF80.0000.0000.0000.3990.6040.9710.5490.6120.8470.5260.5800.4550.5450.5550.4580.4910.5050.5440.5050.7030.5570.5360.4340.5360.4470.4190.6460.9960.0000.0000.9960.4860.2450.3500.5760.4460.2550.2550.6270.3980.3980.2550.5670.2730.4010.4550.4461.0000.3740.3410.3980.2550.6880.5430.3980.2550.5610.5750.2550.2550.2570.3260.2880.4100.4420.4260.3650.4050.5150.4120.3840.7180.3621.0000.4520.4880.5150.3190.3710.7180.084
post_VRF140.0000.0140.0000.3920.6060.9500.5380.5460.5350.6620.6070.5760.5170.4930.5910.5070.5900.7270.5520.5260.6250.5980.5080.4180.4750.6560.6080.9960.0000.0000.9960.5590.2600.3010.3560.4180.2550.2550.6540.2550.2550.2550.5400.5130.4350.4390.4701.0000.3610.3650.2550.3980.5460.3610.2550.2550.2620.5180.2550.2550.4630.5380.3100.4600.4490.5680.5180.6100.5880.5050.3980.5340.4020.4521.0000.6020.4740.5360.4320.5340.145
post_VRF190.0000.0000.0000.4330.5440.9680.5300.6310.6120.5240.4600.5580.4400.4650.5110.4010.5110.6580.5260.5040.5740.5840.4580.4570.3820.6000.6690.9960.0000.0000.9960.5200.2450.3010.2730.2550.5460.3980.6270.3980.3980.2550.4480.2730.3600.3610.3661.0000.3370.5200.3980.3980.2550.5760.3980.5460.4760.4300.5460.2550.3310.3600.3710.3160.3450.2990.3930.4230.6610.5150.4430.7650.3110.4880.6021.0000.3950.4210.5400.7650.167
post_VRF210.0000.0000.0000.4850.5290.9190.5310.4850.4810.6050.4910.5080.5760.3790.5400.5400.5960.6510.4950.5310.5650.5510.4970.5940.4670.5970.5250.9960.0000.0000.9960.4180.4060.4020.3850.3120.3980.5460.3220.3510.3510.5110.3700.3100.3840.3320.3691.0000.4800.3650.3510.4590.3510.4250.3510.3980.4060.3010.3980.5460.4240.3630.3100.4750.4080.4320.4400.5420.4820.2030.5330.6860.4480.5150.4740.3951.0000.4270.4420.6860.088
post_VRF240.0000.0560.0000.4520.6800.9300.4500.4510.3730.5560.6490.5880.5410.4620.5830.6840.5480.5200.4970.5090.4130.5800.5500.5060.4830.5880.5090.9960.0000.0000.9960.2680.2760.4140.3410.3120.1890.1890.2420.1890.1890.2780.3060.6170.3280.3900.6191.0000.2670.3120.1890.1890.4590.3960.1890.5460.1940.3430.5460.5460.9020.7790.4700.4170.6460.4110.4650.8400.4860.5960.4220.6540.3800.3190.5360.4210.4271.0000.5280.6540.208
post_VRF270.0000.0430.0000.4810.5240.8800.5680.5680.4490.6220.5950.5540.4840.5520.6060.4660.6170.6210.5410.5000.6260.5250.5270.6130.4580.4270.5490.9960.0000.0000.9960.3550.2600.5180.5840.3650.4590.2820.4480.3980.3980.5290.4500.2170.4670.3980.4151.0000.4500.4150.3980.3980.3980.6360.3980.4590.5610.3660.4590.2820.3660.3350.6120.2810.3660.2170.4430.3120.3640.4820.3580.6790.4810.3710.4320.5400.4420.5281.0000.6790.154
EHQ1_F0.0000.0000.0000.8130.6900.9630.8050.7600.6750.6670.7150.6270.6550.6710.8350.6950.6080.6710.7410.8850.7360.6940.6180.6240.7230.6120.7260.9980.0000.0000.9980.7470.8390.6690.6760.5610.9981.0000.4330.6760.6760.6760.6480.4060.7580.5230.3981.0000.6000.7580.6741.0000.4280.6740.6740.4280.6740.6320.4280.5270.6820.7000.6910.7690.7410.6320.6730.6360.7350.9980.7221.0000.7450.7180.5340.7650.6860.6540.6791.0000.090
mistake_flag0.0390.0601.0000.0680.2080.2330.0000.1280.0450.1250.1430.1380.1270.0440.1600.0520.1090.0780.0940.1190.1220.1760.1110.0910.0680.0550.1220.2300.0260.0000.2300.0000.0150.0670.0000.0190.0000.0610.0210.0000.0000.0000.0660.0000.0310.0740.0001.0000.0000.0240.0000.0460.0090.1020.0000.0000.0000.0690.0000.0000.0510.0730.0000.0340.0290.0340.1490.0430.0730.0250.1310.0900.0930.0840.1450.1670.0880.2080.1540.0901.000

Missing values

2023-10-20T16:27:20.479981image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
A simple visualization of nullity by column.
2023-10-20T16:27:21.136125image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-10-20T16:27:22.207046image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

ptcptrial_setlevelCountertime_msfeedbackTypecorrectCounterPARTICIPANT IDHANDEDNESSSEXAGEprepostbody°pre_csq1pre_csq2pre_csq3pre_csq4pre_csq5pre_csq6pre_csq7pre_csq8pre_csq9pre_csq10pre_csq11pre_csq12pre_csq13pre_csq14pre_csq15pre_csq16post_csq1post_csq2post_csq3post_csq4post_csq5post_csq6post_csq7post_csq8post_csq9post_csq10post_csq11post_csq12post_csq13post_csq14post_csq15post_csq16EHQ1EHQ2EHQ3EHQ4EHQ5EHQ6EHQ7EHQ8EHQ9EHQ10EHQIEHQIIEHQ_Fpost_VRF1post_VRF2post_VRF3post_VRF4post_VRF5post_VRF6post_VRF7post_VRF8post_VRF9post_VRF10post_VRF11post_VRF12post_VRF13post_VRF14post_VRF15post_VRF16post_VRF17post_VRF18post_VRF19post_VRF20post_VRF21post_VRF22post_VRF23post_VRF24post_VRF25post_VRF26post_VRF27EHQ1_Fmistake_flag
0tsvr2710.04776.0incongruent0.0TSVR_27-backupRF231139236.0111111111111111112111111111111114554454555332425261764225352667613373133.020
1tsvr2711.05513.0congruent1.0TSVR_27-backupRF231139236.0111111111111111112111111111111114554454555332425261764225352667613373133.020
2tsvr2712.05493.0congruent2.0TSVR_27-backupRF231139236.0111111111111111112111111111111114554454555332425261764225352667613373133.020
3tsvr2713.07875.0none3.0TSVR_27-backupRF231139236.0111111111111111112111111111111114554454555332425261764225352667613373133.020
4tsvr2714.04288.0incongruentNaNTSVR_27-backupRF231139236.0111111111111111112111111111111114554454555332425261764225352667613373133.021
5tsvr2715.011431.0incongruent4.0TSVR_27-backupRF231139236.0111111111111111112111111111111114554454555332425261764225352667613373133.020
6tsvr2716.05231.0none5.0TSVR_27-backupRF231139236.0111111111111111112111111111111114554454555332425261764225352667613373133.020
7tsvr2717.07626.0congruent6.0TSVR_27-backupRF231139236.0111111111111111112111111111111114554454555332425261764225352667613373133.020
8tsvr2718.07589.0none7.0TSVR_27-backupRF231139236.0111111111111111112111111111111114554454555332425261764225352667613373133.020
9tsvr2719.07926.0none8.0TSVR_27-backupRF231139236.0111111111111111112111111111111114554454555332425261764225352667613373133.020
ptcptrial_setlevelCountertime_msfeedbackTypecorrectCounterPARTICIPANT IDHANDEDNESSSEXAGEprepostbody°pre_csq1pre_csq2pre_csq3pre_csq4pre_csq5pre_csq6pre_csq7pre_csq8pre_csq9pre_csq10pre_csq11pre_csq12pre_csq13pre_csq14pre_csq15pre_csq16post_csq1post_csq2post_csq3post_csq4post_csq5post_csq6post_csq7post_csq8post_csq9post_csq10post_csq11post_csq12post_csq13post_csq14post_csq15post_csq16EHQ1EHQ2EHQ3EHQ4EHQ5EHQ6EHQ7EHQ8EHQ9EHQ10EHQIEHQIIEHQ_Fpost_VRF1post_VRF2post_VRF3post_VRF4post_VRF5post_VRF6post_VRF7post_VRF8post_VRF9post_VRF10post_VRF11post_VRF12post_VRF13post_VRF14post_VRF15post_VRF16post_VRF17post_VRF18post_VRF19post_VRF20post_VRF21post_VRF22post_VRF23post_VRF24post_VRF25post_VRF26post_VRF27EHQ1_Fmistake_flag
2351tsvr07325.03584.0congruent22.0TSVR_07RF25576936.7<NA>221121111111121<NA>312121111311321443433353333NaN366545223456637364735736464.0NaN0
2352tsvr07326.03658.0incongruentNaNTSVR_07RF25576936.7<NA>221121111111121<NA>312121111311321443433353333NaN366545223456637364735736464.0NaN1
2353tsvr07327.03228.0incongruent23.0TSVR_07RF25576936.7<NA>221121111111121<NA>312121111311321443433353333NaN366545223456637364735736464.0NaN0
2354tsvr07328.04116.0none24.0TSVR_07RF25576936.7<NA>221121111111121<NA>312121111311321443433353333NaN366545223456637364735736464.0NaN0
2355tsvr07329.03855.0noneNaNTSVR_07RF25576936.7<NA>221121111111121<NA>312121111311321443433353333NaN366545223456637364735736464.0NaN1
2356tsvr07330.04394.0congruentNaNTSVR_07RF25576936.7<NA>221121111111121<NA>312121111311321443433353333NaN366545223456637364735736464.0NaN1
2357tsvr07331.03875.0congruent25.0TSVR_07RF25576936.7<NA>221121111111121<NA>312121111311321443433353333NaN366545223456637364735736464.0NaN0
2358tsvr07332.03351.0congruent26.0TSVR_07RF25576936.7<NA>221121111111121<NA>312121111311321443433353333NaN366545223456637364735736464.0NaN0
2359tsvr07333.03232.0incongruent27.0TSVR_07RF25576936.7<NA>221121111111121<NA>312121111311321443433353333NaN366545223456637364735736464.0NaN0
2360tsvr07334.03666.0incongruent28.0TSVR_07RF25576936.7<NA>221121111111121<NA>312121111311321443433353333NaN366545223456637364735736464.0NaN0